Detecting Downhole Events Using Acoustic Frequency Domain Features

ABSTRACT

A method of detecting an event within a wellbore includes obtaining a sample data set, determining a plurality of frequency domain features of the sample data set, comparing the plurality of frequency domain features with an event signature, determining that the plurality of frequency domain features matches the thresholds, ranges, or both of the event signature, and determining the presence of the event within the wellbore based on determining that the plurality of frequency domain features match the thresholds, ranges, or both of the event signature. The sample data set is a sample of an acoustic signal originating within a wellbore including a fluid. The sample data set is representative of the acoustic signal across a frequency spectrum. The event signature includes a plurality of thresholds, ranges, or both corresponding to the plurality of frequency domain features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 35 U.S.C. § 371 national stage application ofPCT/EP2017/058292 filed Apr. 6, 2017 and entitled “Detecting DownholeEvents Using Acoustic Frequency Domain Features,” which claims priorityto GB Application No. 1605969.3 filed Apr. 7, 2016 and entitled“Detecting Downhole Sand Ingress Locations,” and which also claimspriority to U.S. 62/437,318 filed Dec. 21, 2016 and entitled “DetectingDownhole Sand Ingress Locations,” all of which are hereby incorporatedherein by reference in their entirety for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

Within a hydrocarbon production well, various fluids such ashydrocarbons, water, gas, and the like can be produced from theformation into the wellbore. The production of the fluid can result inthe movement of the fluids in various downhole regions, including withthe subterranean formation, from the formation into the wellbore, andwithin the wellbore itself. For example, some subterranean formationscan release solids, generally referred to as “sand,” that can beproduced along with the fluids into the wellbore. These solids can causea number of problems including erosion, clogging of wells, contaminationand damage of the surface equipment, and the like. Sand production tendsto be present when the producing formations are formed from weaklyconsolidated sand stones with low unconfined compressive strength. Insuch formations, sand control failures can lead to significant sandproduction, which can result in the need to choke back production fromthe well to bring sand production down to acceptable levels. This canlead to reduced oil production, and potentially result in a deferral ofover 75% of the production from the well.

Efforts have been made to detect the movement of various fluidsincluding those with particles in them within the wellbore. For example,efforts to detect sand have been made using acoustic point sensorsplaced at the surface of the well and clamped onto the production pipe.Produced sand particles passing through the production pipe, along withthe produced fluids (e.g., oil, gas or water), contact the walls of thepipe, especially at the bends and elbows of the production pipe. Suchcontact creates stress waves that are captured as sound signals by theacoustic sensors mounted on the pipe wall. However, these detectionmethods only capture the presence of the sand at or near the surfaceequipment and are qualitative at best (e.g., indicating the presence ofsand only).

BRIEF SUMMARY OF THE DISCLOSURE

In an embodiment, a method of detecting sand inflow into a wellborecomprises obtaining a sample data set, detecting a broadband signalwithin the sample data set, comparing the broadband signal with a signalreference, determining that the broadband signal meets or exceeds thesignal reference, and determining the presence of sand inflow into thewellbore based on determining that the broadband signal meets or exceedsthe signal reference. The sample data set is a sample of an acousticsignal originating within a wellbore comprising a fluid, and thebroadband signal comprises frequencies greater than about 0.5 kHz.

Detecting a broadband signal can include determining a spectral centroidof the sample data set, determining a spectral spread of the sample dataset, determining that the spectral spread is greater than a spectralspread threshold, and determining that the spectral centroid is greaterthan a spectral centroid threshold. The signal reference can include aspectral centroid threshold and a spectral spread threshold. Determiningthe presence of sand inflow into the wellbore can be based ondetermining that the spectral centroid is greater than a spectralcentroid threshold and determining that the spectral spread is greaterthan a spectral spread threshold.

Detecting a broadband signal can also or alternatively include frequencyfiltering the sample data set into a plurality of frequency bands, anddetermining that at least one frequency band of the plurality offrequency bands comprises frequencies greater than about 0.5 kHz. Thesignal reference can include a baseline acoustic signal, and determiningthat the broadband signal meets or exceeds the signal reference caninclude determining that frequencies in the at least one frequency bandcomprising frequencies greater than about 0.5 kHz have an intensitygreater than corresponding frequencies in the same at least onefrequency band of the baseline acoustic signal.

In an embodiment, a system of detecting sand inflow into a wellborecomprises a processor unit comprising a processor and a memory. Theprocessor unit is adapted for signal communication with a receiver, andthe memory comprises an analysis application, that when executed on theprocessor, configures the processor to: receive, from the receiver, asample data set, the sample data set being a sample of an acousticsignal from a wellbore that comprises a fluid, detect a broadband signalwithin the sample data set, compare the broadband signal with a signalreference, determine that the broadband signal meets or exceeds thesignal reference, determine the presence of sand inflow into thewellbore based on determining that the broadband signal meets or exceedsthe signal reference, and provide an output indicative of thedetermination of the presence of the sand inflow. The broadband signalcan comprise frequencies greater than about 0.5 kHz.

In an embodiment, a method of detecting sand inflow into a wellborecomprises filtering an acoustic data set using a spatial filter toobtain a first data sample in the time domain, transforming the firstdata sample to a frequency domain to produce a second data sample,determining a spectral centroid of the second data sample, determining aspectral spread of the second data sample, determining that the spectralcentroid is greater than a spectral centroid threshold, determining thatthe spectral spread is greater than a spectral spread threshold, anddetermining the presence of sand entering the wellbore at the defineddepth based on determining that the spectral centroid is greater than aspectral centroid threshold and determining that the spectral spread isgreater than a spectral spread threshold. The acoustic data can beobtained from the wellbore, and the first data sample can be indicativeof an acoustic sample over a defined depth in the wellbore.

In an embodiment, a system for processing wellbore data comprises areceiver unit comprising a processor and a memory, where a processingapplication is stored in the memory. The receiver unit is configured toreceive a signal from a sensor disposed in a wellbore. The processingapplication, when executed on the processor, configures the processorto: receive the signal from the sensor, determine a plurality offrequency domain features of the signal across the frequency spectrum,and generate an output comprising the plurality of frequency domainfeatures. The signal comprises an indication of an acoustic signalreceived at one or more depths within the wellbore, and the signal isindicative of the acoustic signal across a frequency spectrum.

In an embodiment, a system for detecting an event within a wellborecomprises a processor unit comprising a processor and a memory, wherethe processor unit is adapted for signal communication with a receiver.The memory comprises an analysis application, that when executed on theprocessor, configures the processor to: receive, from the receiver, asignal comprising a plurality of frequency domain features, compare theplurality of frequency domain features with one or more eventsignatures, determine that the plurality of frequency domain featuresmatch at least one event signature of the one or more event signatures,determine the occurrence of at least one event based on thedetermination that the plurality of frequency domain features match theat least one event signature, and generate an output of the occurrenceof the at least one event based on the determination. The frequencydomain features are indicative of an acoustic signal within a wellbore,and the frequency domain features are indicative of the acoustic signalacross a frequency spectrum. The one or more event signatures comprisethresholds or ranges for each of the plurality of frequency domainfeatures.

In an embodiment, a method of detecting an event within a wellborecomprises obtaining a sample data set, determining a plurality offrequency domain features of the sample data set, comparing theplurality of frequency domain features with an event signature,determining that the plurality of frequency domain features matches thethresholds, ranges, or both of the event signature, and determining thepresence of the event within the wellbore based on determining that theplurality of frequency domain features match the thresholds, ranges, orboth of the event signature. The sample data set is a sample of anacoustic signal originating within a wellbore comprising a fluid, andthe sample data set is representative of the acoustic signal across afrequency spectrum. The event signature comprises a plurality ofthresholds, ranges, or both corresponding to the plurality of frequencydomain features.

These and other features will be more clearly understood from thefollowing detailed description taken in conjunction with theaccompanying drawings and claims.

Embodiments described herein comprise a combination of features andadvantages intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical advantages of the invention inorder that the detailed description of the invention that follows may bebetter understood. The various characteristics described above, as wellas other features, will be readily apparent to those skilled in the artupon reading the following detailed description, and by referring to theaccompanying drawings. It should be appreciated by those skilled in theart that the conception and the specific embodiments disclosed may bereadily utilized as a basis for modifying or designing other structuresfor carrying out the same purposes of the invention. It should also berealized by those skilled in the art that such equivalent constructionsdo not depart from the spirit and scope of the invention as set forth inthe appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of the preferred embodiments of theinvention, reference will now be made to the accompanying drawings inwhich:

FIG. 1 is a schematic, cross-sectional illustration of a downholewellbore environment according to an embodiment.

FIG. 2 is a schematic view of an embodiment of a wellbore tubular withsand ingress according to an embodiment.

FIGS. 3A and 3B are a schematic, cross-sectional views of embodiments ofa well with a wellbore tubular having an optical fiber associatedtherewith.

FIG. 4 is an exemplary frequency filtered acoustic intensity graphversus time over three frequency bands.

FIG. 5 is another exemplary frequency filtered acoustic intensity graphversus time over five frequency bands.

FIG. 6 illustrates an embodiment of a schematic processing flow for anacoustic signal.

FIG. 7 illustrates an exemplary graph of acoustic power versus frequencyfor a plurality of downhole events.

FIG. 8 is a generic representation of a sand log according to anembodiment.

FIG. 9 schematically illustrates a flowchart of a method for detectingsand ingress in a wellbore according to an embodiment.

FIG. 10 schematically illustrates a computer that can be used to carryout various steps according to an embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

Disclosed herein is a new real time signal processing architecture thatallows for the identification of various downhole events including gasinflux detection, downhole leak detection, well-barrier integritymonitoring, fluid inflow, and the identification of in-well sand ingresszones in real time or near real time. In some embodiments, the systemallows for a quantitative measurement of various fluid flows such as arelative concentration of in-well sand ingress. As used herein, the term“real time” refers to a time that takes into account variouscommunication and latency delays within a system, and can includeactions taken within about ten seconds, within about thirty seconds,within about a minute, within about five minutes, or within about tenminutes of the action occurring. Various sensors (e.g., distributedfiber optic acoustic sensors, etc.) can be used to obtain an acousticsampling at various points along the wellbore. The acoustic sample canthen be processed using signal processing architecture with variousfeature extraction techniques (e.g., spectral feature extractiontechniques) to obtain a measure of one or more frequency domain featuresthat enable selectively extracting the acoustic signals of interest frombackground noise and consequently aiding in improving the accuracy ofthe identification of the movement of fluids and/or solids (e.g., sandingress locations, gas influx locations, constricted fluid flowlocations, etc.) in real time. As used herein, various frequency domainfeatures can be obtained from the acoustic signal, and in some contextsthe frequency domain features can also be referred to as spectralfeatures or spectral descriptors. The signal processing techniquesdescribed herein can also help to address the big-data problem throughintelligent extraction of data (rather than crude decimation techniques)to considerably reduce real time data volumes at the collection andprocessing site (e.g., by over 100 times, over 500 times, or over 1000times, or over 10,000 times reduction).

The acoustic signal can be obtained in a manner that allows for a signalto be obtained along the entire wellbore or a portion of interest. Whilesurface clamp-on acoustic detectors can provide an indication thatcertain events, such as downhole sanding, are occurring, they do notprovide information about the depth in the production zone contributingto events such as sanding. Further, the methodology adopted forprocessing the clamp-on detector data for identifying the events fromother acoustic “background” noise have only yielded qualitative andoften inconsistent results. A number of other technical limitationscurrently hinder direct application of the technology for real timein-well acoustic detection. Fiber optic distributed acoustic sensors(DAS) capture acoustic signals resulting from downhole events such asgas influx, fluid flow past restrictions, sand ingress, and the like aswell as other background acoustics as well. This mandates the need for arobust signal processing procedure that distinguishes sand ingresssignals from other noise sources to avoid false positives in theresults. This in turn results in a need for a clearer understanding ofthe acoustic fingerprint of in-well event of interest (e.g., sandingress, etc.) in order to be able to segregate a noise resulting froman event of interest from other ambient acoustic background noise. Asused herein, the resulting acoustic fingerprint of a particular eventcan also be referred to as a spectral signature, as described in moredetail herein.

Further, reducing deferrals resulting from one or more events such assand ingress and facilitating effective remediation relies uponnear-real time decision support to inform the operator of the events.There is currently no technology/signal processing for DAS thatsuccessfully distinguishes and extracts event locations, let alone innear real time.

In terms of data processing and loads, DAS acquisition units producelarge data volumes (typically around 1 TB/hour) creating complexities indata handling, data transfer, data processing and storage. There iscurrently no method of intelligently extracting useful information toreduce data volumes in real time for immediate decision support. Thisimposes complexity in real time data transmission to shore and dataintegration into existing IT platforms due to data bandwidth limitationsand the data has to be stored in hard drives that are shipped back toshore for interpretation and analysis. In addition, this increases theinterpretation turnaround time (typically a few weeks to months) beforeany remediation efforts can be taken resulting in deferred production.

The ability to identify various events in the wellbore may allow forvarious actions to be taken (remediation procedures) in response to theevents. For example, a well can be shut in, production can be increasedor decreased, and/or remedial measures can be taken in the wellbore, asappropriate based on the identified event(s). An effective response,when needed, benefits not just from a binary yes/no output of anidentification of in-well events but also from a measure of relativeamount of fluids and/or solids (e.g., concentrations of sand, amount ofgas influx, amount of fluid flow past a restriction, etc.) from each ofthe identified zones so that zones contributing the greatest fluidand/or solid amounts can be acted upon first to improve or optimizeproduction. For example, when a leak is detected past a restriction, arelative flow rate of the leak may allow for an identification of thetiming in working to plug the leak (e.g., small leaks may not need to befixed, larger leaks may need to be fixed with a high priority, etc.).

As described herein, spectral descriptors can be used with DAS acousticdata processing in real time to provide various downhole surveillanceapplications. More specifically, the data processing techniques can beapplied for various for downhole fluid profiling such as fluidinflow/outflow detection, fluid phase segregation, well integritymonitoring, in well leak detection (e.g., downhole casing and tubingleak detection, leaking fluid phase identification, 4 etc.), annularfluid flow diagnosis; overburden monitoring, fluid flow detection behinda casing, fluid induced hydraulic fracture detection in the overburden,and the like. Application of the signal processing technique with DASfor downhole surveillance provides a number of benefits includingimproving reservoir recovery by monitoring efficient drainage ofreserves through downhole fluid surveillance (well integrity andproduction inflow monitoring), improving well operating envelopesthrough identification of drawdown levels (e.g., gas, sand, water,etc.), facilitating targeted remedial action for efficient sandmanagement and well integrity, reducing operational risk through theclear identification of anomalies and/or failures in well barrierelements.

In some embodiments, use of the systems and methods described herein mayprovide knowledge of the zones contributing to sanding and theirrelative concentrations, thereby potentially allowing for improvedremediation actions based on the processing results. The methods andsystems disclosed herein can also provide information on the variabilityof the amount of sand being produced by the different sand influx zonesas a function of different production rates, different productionchokes, and downhole pressure conditions, thereby enabling choke control(e.g., automated choke control) for controlling sand production.Embodiments of the systems and methods disclosed herein also allow for acomputation of the relative concentrations of sand ingress into thewellbore, thereby offering the potential for more targeted and effectiveremediation.

As disclosed herein, embodiments of the data processing techniques use asequence of real time digital signal processing steps to isolate andextract the acoustic signal resulting from sand ingress from backgroundnoise, and allow real time detection of downhole sand ingress zonesusing distributed fiber optic acoustic sensor data as the input datafeed.

Referring now to FIG. 1, an example of a wellbore operating environment100 is shown. As will be described in more detail below, embodiments ofcompletion assemblies comprising distributed acoustic sensor (DAS)system in accordance with the principles described herein can bepositioned in environment 100.

As shown in FIG. 1, exemplary environment 100 includes a wellbore 114traversing a subterranean formation 102, casing 112 lining at least aportion of wellbore 114, and a tubular 120 extending through wellbore114 and casing 112. A plurality of spaced screen elements or assemblies118 are provided along tubular 120. In addition, a plurality of spacedzonal isolation device 117 and gravel packs 122 are provided betweentubular 120 and the sidewall of wellbore 114. In some embodiments, theoperating environment 100 includes a workover and/or drilling rigpositioned at the surface and extending over the wellbore 114.

In general, the wellbore 114 can be drilled into the subterraneanformation 102 using any suitable drilling technique. The wellbore 114can extend substantially vertically from the earth's surface over avertical wellbore portion, deviate from vertical relative to the earth'ssurface over a deviated wellbore portion, and/or transition to ahorizontal wellbore portion. In general, all or portions of a wellboremay be vertical, deviated at any suitable angle, horizontal, and/orcurved. In addition, the wellbore 114 can be a new wellbore, an existingwellbore, a straight wellbore, an extended reach wellbore, a sidetrackedwellbore, a multi-lateral wellbore, and other types of wellbores fordrilling and completing one or more production zones. As illustrated,the wellbore 114 includes a substantially vertical producing section150, which is an open hole completion (i.e., casing 112 does not extendthrough producing section 150). Although section 150 is illustrated as avertical and open hole portion of wellbore 114 in FIG. 1, embodimentsdisclosed herein can be employed in sections of wellbores having anyorientation, and in open or cased sections of wellbores. The casing 112extends into the wellbore 114 from the surface and is cemented withinthe wellbore 114 with cement 111.

Tubular 120 can be lowered into wellbore 114 for performing an operationsuch as drilling, completion, workover, treatment, and/or productionprocesses. In the embodiment shown in FIG. 1, the tubular 120 is acompletion assembly string including a distributed acoustic sensor (DAS)sensor coupled thereto. However, in general, embodiments of the tubular120 can function as a different type of structure in a wellboreincluding, without limitation, as a drill string, casing, liner, jointedtubing, and/or coiled tubing. Further, the tubular 120 may operate inany portion of the wellbore 114 (e.g., vertical, deviated, horizontal,and/or curved section of wellbore 114). Embodiments of DAS systemsdescribed herein can be coupled to the exterior of the tubular 120, orin some embodiments, disposed within an interior of the tubular 120, asshown in FIGS. 3A and 3B. When the DAS is coupled to the exterior of thetubular 120, the DAS can be positioned within a control line, controlchannel, or recess in the tubular 120. In some embodiments, a sandcontrol system can include an outer shroud to contain the tubular 120and protect the system during installation. A control line or channelcan be formed in the shroud and the DAS system can be placed in thecontrol line or channel.

The tubular 120 extends from the surface to the producing zones andgenerally provides a conduit for fluids to travel from the formation 102to the surface. A completion assembly including the tubular 120 caninclude a variety of other equipment or downhole tools to facilitate theproduction of the formation fluids from the production zones. Forexample, zonal isolation devices 117 are used to isolate the variouszones within the wellbore 114. In this embodiment, each zonal isolationdevice 117 can be a packer (e.g., production packer, gravel pack packer,frac-pac packer, etc.). The zonal isolation devices 117 can bepositioned between the screen assemblies 118, for example, to isolatedifferent gravel pack zones or intervals along the wellbore 114 fromeach other. In general, the space between each pair of adjacent zonalisolation devices 117 defines a production interval.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. The screen assemblies 118 canbe of the type known as “wire-wrapped”, which are made up of a wireclosely wrapped helically about a wellbore tubular, with a spacingbetween the wire wraps being chosen to allow fluid flow through thefilter media while keeping particulates that are greater than a selectedsize from passing between the wire wraps. Other types of filter mediacan also be provided along the tubular 120 and can include any type ofstructures commonly used in gravel pack well completions, which permitthe flow of fluids through the filter or screen while restricting and/orblocking the flow of particulates (e.g. other commercially-availablescreens, slotted or perforated liners or pipes; sintered-metal screens;sintered-sized, mesh screens; screened pipes; prepacked screens and/orliners; or combinations thereof). A protective outer shroud having aplurality of perforations therethrough may be positioned around theexterior of any such filter medium.

The gravel packs 122 are formed in the annulus 119 between the screenelements 118 (or tubular 120) and the sidewall of the wellbore 114 in anopen hole completion. In general, the gravel packs 122 compriserelatively coarse granular material placed in the annulus to form arough screen against the ingress of sand into the wellbore while alsosupporting the wellbore wall. The gravel pack 122 is optional and maynot be present in all completions.

The fluid flowing into the tubular 120 may comprise more than one fluidcomponent. Typical components include natural gas, oil, water, steam,and/or carbon dioxide. The relative proportions of these components canvary over time based on conditions within the formation 102 and thewellbore 114. Likewise, the composition of the fluid flowing into thetubular 120 sections throughout the length of the entire productionstring can vary significantly from section to section at any given time.

As fluid is produced into the wellbore 114 and into the completionassembly string, various solid particles present in the formation can beproduced along with a fluid (e.g., oil, water, natural gas, etc.). Suchsolid particles are referred to herein as “sand,” and can include anysolids originating within the subterranean formation regardless of sizeor composition. As the sand enters the wellbore 114, it may createacoustic sounds that can be detected using an acoustic sensor such as aDAS system. Similarly, the flow of the various fluids into the wellbore114 and/or through the wellbore 114 can create acoustic sounds that canbe detected using the acoustic sensor such as the DAS system. Each typeof event such as the different fluid flows and fluid flow locations canproduce an acoustic signature with unique frequency domain features.

In FIG. 1, the DAS comprises an optical fiber 162 based acoustic sensingsystem that uses the optical backscatter component of light injectedinto the optical fiber for detecting acoustic perturbations (e.g.,dynamic strain) along the length of the fiber 162. The light can begenerated by a light generator or source 166 such as a laser, which cangenerate light pulses. The optical fiber 162 acts as the sensor elementwith no additional transducers in the optical path, and measurements canbe taken along the length of the entire optical fiber 162. Themeasurements can then be detected by an optical receiver such as sensor164 and selectively filtered to obtain measurements from a given depthpoint or range, thereby providing for a distributed measurement that hasselective data for a plurality of zones along the optical fiber 162 atany given time. In this manner, the optical fiber 162 effectivelyfunctions as a distributed array of microphones spread over the entirelength of the optical fiber 162, which typically spans at least theproduction zone 150 of the wellbore 114, to detect downhole acoustics.

The light reflected back up the optical fiber 162 as a result of thebackscatter can travel back to the source, where the signal can becollected by a sensor 164 and processed (e.g., using a processor 168).In general, the time the light takes to return to the collection pointis proportional to the distance traveled along the optical fiber 162.The resulting backscattered light arising along the length of theoptical fiber 162 can be used to characterize the environment around theoptical fiber 162. The use of a controlled light source 166 (e.g.,having a controlled spectral width and frequency) may allow thebackscatter to be collected and any disturbances along the length of theoptical fiber 162 to be analyzed. In general, any acoustic or dynamicstrain disturbances along the length of the optical fiber 162 can resultin a change in the properties of the backscattered light, allowing for adistributed measurement of both the acoustic magnitude, frequency and insome cases of the relative phase of the disturbance.

An acquisition device 160 can be coupled to one end of the optical fiber162. As discussed herein, the light source 166 can generate the light(e.g., one or more light pulses), and the sensor 164 can collect andanalyze the backscattered light returning up the optical fiber 162. Insome contexts, the acquisition device 160 including the light source 166and the sensor 164 can be referred to as an interrogator. In addition tothe light source 166 and the sensor 164, the acquisition device 160generally comprises a processor 168 in signal communication with thesensor 164 to perform various analysis steps described in more detailherein. While shown as being within the acquisition device 160, theprocessor can also be located outside of the acquisition device 160including being located remotely from the acquisition device 160. Thesensor 164 can be used to obtain data at various rates and may obtaindata at a sufficient rate to detect the acoustic signals of interestwith sufficient bandwidth. In an embodiment, depth resolution ranges ofbetween about 1 meter and about 10 meters can be achieved. While thesystem 100 described herein can be used with a DAS system to acquire anacoustic signal for a location or depth range in the wellbore 114, ingeneral, any suitable acoustic signal acquisition system can be usedwith the processing steps disclosed herein. For example, variousmicrophones or other sensors can be used to provide an acoustic signalat a given location based on the acoustic signal processing describedherein. The benefit of the use of the DAS system is that an acousticsignal can be obtained across a plurality of locations and/or across acontinuous length of the wellbore 114 rather than at discrete locations.

Specific spectral signatures can be determined for each event byconsidering one or more frequency domain features. The resultingspectral signatures can then be used along with processed acousticsignal data to determine if an event is occurring at a depth range ofinterest. The spectral signatures can be determined by considering thedifferent types of movement and flow occurring within a wellbore andcharacterizing the frequency domain features for each type of movement.

Sand ingress can be considered first. As schematically illustrated inFIG. 2 and shown in the cross-sectional illustrations in FIGS. 3A and3B, sand 202 can flow from the formation 102 into the wellbore 114 andthen into the tubular 120. As the sand 202 flows into the tubular 120,it can collide against the inner surface 204 of the tubular 120, andwith the fiber itself in cases where the fiber is displaced within thetubular, in a random fashion. Without being limited by this or anyparticular theory, the intensity of the collisions depends on theeffective mass and the rate of change in the velocity of the impingingsand particles. This can depend on a number of factors including,without limitation, the direction of travel of the sand 202 in thewellbore 114 and/or tubular 120. The resulting random impacts canproduce a random, broadband acoustic signal that can be captured on theoptical fiber 162 coupled (e.g., strapped) to the tubular 120. Therandom excitation response tends to have a broadband acoustic signalwith excitation frequencies extending up to the high frequency bands,for example, up to and beyond about 5 kHz depending on the size of thesand particles. In general, larger particle sizes may produce higherfrequencies. The intensity of the acoustic signal may be proportional tothe concentration of sand 202 generating the excitations such that anincreased broad band power intensity can be expected at increasing sand202 concentrations. In some embodiments, the resulting broadbandacoustic signals that can be identified can include frequencies in therange of about 5 Hz to about 10 kHz, frequencies in the range of about 5Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in therange of about 500 Hz to about 5 kHz. Any frequency ranges between thelower frequencies values (e.g., 5 Hz, 50 Hz, 500 Hz, etc.) and the upperfrequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be used todefine the frequency range for a broadband acoustic signal.

The sand 202 entering the wellbore 114 can be carried within a carrierfluid 206, and the carrier fluid 206 can also generate high intensityacoustic background noise when entering the wellbore 114 due to theturbulence associated with the fluid flowing into the tubular 120. Thisbackground noise generated by the turbulent fluid flow is generallyexpected to be predominantly in a lower frequency region. For example,the fluid inflow acoustic signals can be between about 0 Hz and about500 Hz, or alternatively between about 0 Hz and about 200 Hz. Anincreased power intensity can be expected at low frequencies resultingfrom increased turbulence in the carrier fluid flow. The backgroundnoises can be detected as superimposed signals on the broad-bandacoustic signals produced by the sand 202 when the sand ingress occurs.

A number of acoustic signal sources can also be considered along withthe types of acoustic signals these sources generate. In general, avariety of signal sources can be considered including fluid flow with orwithout sand through the formation 102, fluid flow with or without sand202 through a gravel pack 122, fluid flow with or without sand within orthrough the tubular 120 and/or sand screen 118, fluid flow with sand 202within or through the tubular 120 and/or sand screen 118, fluid flowwithout sand 202 into the tubular 120 and/or sand screen 118, gas/liquidinflow, hydraulic fracturing, fluid leaks past restrictions (e.g., gasleaks, liquid leaks, etc.) mechanical instrumentation and geophysicalacoustic noises and potential point reflection noise within the fibercaused by cracks in the fiber optic cable/conduit under investigation.

For the flow of fluid 206, with the potential for sand 202 to be carriedwith the flowing fluid 206, in the formation 102, the likelihood thatany resulting acoustic signal would be captured by the optical fiber 162is considered low. Further, the resulting acoustic signal would likelybe dominated by low frequencies resulting from turbulent fluid flow.Similarly, the fluid flowing within the gravel pack 122 would likelyflow with a low flow speed and therefore limit the generation andintensity of any acoustic signals created by the sand 202. Thus, theacoustic response would be expected to occur in the lower frequencyrange.

For the flow of fluid 206 with or without sand 202 through a gravel pack122, the likelihood that any resulting acoustic signal would be capturedby the acoustic sensor is also considered low. Further, the resultingacoustic signal would likely be dominated by low frequencies resultingfrom turbulent fluid flow.

For the flow of fluid 206 with or without sand 202 within or through thetubular 120, the likelihood of capturing an acoustic signal isconsidered high due to the proximity of the source of the acousticsignals to the optical fiber 162 coupled to the tubular 120. This typeof flow can occur as the fluid 206 containing sand 202 flows within thetubular 120. Such flow would result in any sand flowing generallyparallel to the inner surface 204 of the tubular 120, which would limitthe generation of high frequency sounds as well as the intensity of anyhigh frequency sounds that are generated. It is expected that theacoustic signals generated from the flow of the fluid 206 through thetubular 120 and/or sand screen 118 may be dominated by low frequencyacoustic signals resulting from turbulent fluid flow.

In an embodiment, the acoustic signal due to fluid 206 containing sand202 within the tubular can be expected to have a rise in acousticintensity from about 0 Hz to about 50 Hz, with a roll-off in powerbetween about 20 Hz to about 50 Hz. An example of a signal of a fluid206 containing sand 202 is shown in FIG. 4, which illustrates frequencyfiltered acoustic intensity in depth versus time graphs for threefrequency bins. As illustrated, three frequency bins represent 5 Hz to20 Hz, 20 Hz to 50 Hz, and 50 Hz to 100 Hz. The acoustic intensity canbe seen in the first bin and second bin, with a nearly undetectableacoustic intensity in the frequency range between 50 Hz and 100 Hz. Thisdemonstrates the acoustic rolloff for the flow of fluid containing sandwithin a wellbore tubular.

Returning to FIGS. 2-3, for the flow of fluid 206 without any sand 202into the tubular 120 and/or sand screen 118, the proximity to theoptical fiber 162 can result in a high likelihood that any acousticsignals generated would be detected by the acoustic sensor. As discussedherein, the flow of fluid 206 alone without any sand 202 is expected toproduce an acoustic signal dominated by low frequency signals due to theacoustic signals being produced by turbulent fluid flow.

For the flow of fluid 206 with sand 202 into the tubular 120 and/or sandscreen 118, the proximity to the optical fiber 162 can result in a highlikelihood that any acoustic signals generated would be detected by theoptical fiber 162. As further discussed herein, the flow of fluid 206with the sand 202 would likely result in an acoustic signal havingbroadband characteristics with excitation frequencies extending up tothe high frequency bands, for example, up to and beyond about 5 kHz.

For the flow of gas into the wellbore, the proximity to the opticalfiber 162 can result in a high likelihood that any acoustic signalsgenerated would be detected by the optical fiber 162. The flow of a gasinto the wellbore would likely result in a turbulent flow over a broadfrequency range. For example, the gas inflow acoustic signals can bebetween about 0 Hz and about 1000 Hz, or alternatively between about 0Hz and about 500 Hz. An increased power intensity may occur betweenabout 300 Hz and about 500 Hz from increased turbulence in the gas flow.An example of the acoustic signal resulting from the influx of gas intothe wellbore is shown in FIG. 5, which illustrates frequency filteredacoustic intensity in depth versus time graphs for five frequency bins.As illustrated, the five frequency bins represent 5 Hz to 50 Hz, 50 Hzto 100 Hz, 100 Hz to 500 Hz, 500 Hz to 2000 Hz, and 2000 Hz to 5000 Hz.The acoustic intensity can be seen in the first three bins withfrequency ranges up to about 500 Hz, with a nearly undetectable acousticintensity in the frequency range above 500 Hz. This demonstrates that atleast a portion of the frequency domain features may not be presentabove 500 Hz, which can help to define the signature of the influx ofgas.

For hydraulic fracturing, the self-induced fracturing of thesubterranean formation due to various formation conditions can create anacoustic signal. The intensity of such signal may be detected by theoptical fiber 162 depending on the distance between the fracture and theoptical fiber 162. The resulting fracture can be expected to produce awide band response having the acoustic energy present in a frequencyband between about 0 Hz to about 400 Hz. Some amount of spectral energycan be expected up to about 1000 Hz. Further, the discrete nature offracturing events may be seen as a nearly instantaneous broadband highenergy event followed by a low-energy, lower frequency fluid flowacoustic signal resulting from fluid flow in response to the fracture.

For the flow of a fluid behind a casing in the wellbore, the proximityof the fluid flow to the optical fiber 162 can result in the acousticsignal being detected. The flow behind the casing can generally becharacterized by a flow of fluid through one or more restrictions basedon a generally narrow or small leak path being present. The flow throughsuch a restriction may be characterized by an increase in spectral powerin a frequency range between about 0 Hz to about 300 Hz with a mainenergy contribution in the range of about 0 Hz to about 100 Hz, orbetween about 0 Hz and about 70 Hz.

For acoustic signals generated by mechanical instrumentation andgeophysical acoustic noises, the sounds can be detected by the opticalfiber 162 in some instances depending on the distance between the soundgeneration and the portion of the optical fiber 162 being used to detectthe sounds. Various mechanical noises would be expected to have lowfrequency sounds. For example, various motors can operate in the 50 Hzto 60 Hz range, and it is expected that the resulting acoustic signalwould have a spectral energy in a narrow band. Various geophysicalsounds may have even lower frequencies. As a result, it is expected thatthe sounds from the mechanical instrumentation and geophysical sourcescan be filtered out based on a low-pass frequency filter.

For point reflection type noises, these are usually broadband in naturebut can occur at spatially confined depths and usually do not span theexpected spatial resolution of the interrogator. These may be removed aspart of the pre-processing steps by spatial averaging or medianfiltering the data through the entire depth of the fiber.

Based on the expected sound characteristics from the potential acousticsignal sources, the acoustic signature of each event can be definedrelative to background noise contributions. For sand ingress, theacoustic signature can be seen as the presence of a distinct broadbandresponse along with the presence of high frequency components in theresulting response. The uniqueness in the signature of sand enablesapplication of selective signal isolation routines to extract therelevant information pertaining to sand ingress acoustics as describedin the following description. Further, the characteristics of theportion of the acoustic signal resulting from the ingress of sand canallow for the location and potentially the nature and amount of sand inthe fluid to be determined. The acoustic signatures of the other eventscan also be determined and used with the processing to enableidentification of each event, even when the events occur at the sametime in the same depth range.

Referring again to FIG. 1, the processor 168 within the acquisitiondevice 160 can be configured to perform various data processing todetect the presence of one or more events along the length of thewellbore 114. The acquisition device 160 can comprise a memory 170configured to store an application or program to perform the dataanalysis. While shown as being contained within the acquisition device160, the memory 170 can comprise one or more memories, any of which canbe external to the acquisition device 160. In an embodiment, theprocessor 168 can execute the program, which can configure the processor168 to filter the acoustic data set spatially, determine one or morefrequency domain features of the acoustic signal, compare the resultingfrequency domain feature values to the acoustic signatures, anddetermine whether or not an event is occurring at the selected locationbased on the analysis and comparison. The analysis can be repeatedacross various locations along the length of the wellbore 114 todetermine the occurrence of one or more events and/or event locationsalong the length of the wellbore 114.

When the acoustic sensor comprises a DAS system, the optical fiber 162can return raw optical data in real time or near real time to theacquisition unit 160. The intensity of the raw optical data isproportional to the acoustic intensity of the sound being measured. Inan embodiment, the raw data can be stored in the memory 170 for varioussubsequent uses. The sensor 164 can be configured to convert the rawoptical data into an acoustic data set. Depending on the type of DASsystem employed, the optical data may or may not be phase coherent andmay be pre-processed to improve the signal quality (e.g., foropto-electronic noise normalization/de-trending single point-reflectionnoise removal through the use of median filtering techniques or eventhrough the use of spatial moving average computations with averagingwindows set to the spatial resolution of the acquisition unit, etc.).

In some cases, instead of producing a signal comprising raw opticaldata, it is also possible for the DAS system to determine the derivativeof the raw optical data to produce a derivative signal.

As shown schematically in FIG. 6, an embodiment of a system fordetecting sand inflow can comprise a data extraction unit 402, aprocessing unit 404, and/or an output or visualization unit 406. Thedata extraction unit 402 can obtain the optical data and perform theinitial pre-processing steps to obtain the initial acoustic informationfrom the signal returned from the wellbore. Various analysis can beperformed including frequency band extraction, frequency analysis and/ortransformation, intensity and/or energy calculations, and/ordetermination of one or more properties of the acoustic data. Followingthe data extraction unit 402, the resulting signals can be sent to aprocessing unit 404. Within the processing unit, the acoustic data canbe analyzed, for example, by being compared to one or more acousticsignatures to determine if an event of interest is present. In someembodiments, the acoustic signatures can define thresholds or ranges offrequencies and/or frequency domain features. The analysis can theninclude comparing one or more thresholds or references to determine if aspecific signal is present. The processing unit 404 can use thedetermination to determine the presence of one or more events (e.g.,sand inflow, gas influx, fluid leaks, etc.) at one or more locationsbased on the presence of an acoustic signal matching one or moreacoustic signatures, and in some embodiments, the presence of theacoustic signal matching the one or more acoustic signatures. Theresulting analysis information can then be sent from the processing unit404 to the output/visualization unit 406 where various information suchas a visualization of the location of the one or more events and/orinformation providing quantification information (e.g., an amount ofsand inflow, a type of fluid influx, an amount of fluid leaking, and thelike) can be visualized in a number of ways. In an embodiment, theresulting event information can be visualized on a well schematic, on atime log, or any other number of displays to aid in understanding wherethe event is occurring, and in some embodiments, to display a relativeamount of the flow of a fluid and/or sand occurring at one or morelocations along the length of the wellbore. While illustrated in FIG. 6as separate units, any two or more of the units shown in FIG. 6 can beincorporated into a single unit. For example, a single unit can bepresent at the wellsite to provide analysis, output, and optionally,visualization of the resulting information.

A number of specific processing steps can be performed to determine thepresence of an event. In an embodiment, the noise detrended “acousticvariant” data can be subjected to an optional spatial filtering stepfollowing the pre-processing steps, if present. This is an optional stepand helps focus primarily on an interval of interest in the wellbore.For example, the spatial filtering step can be used to focus on aproducing interval where there is maximum likelihood of sand ingresswhen a sand ingress event is being examined. In an embodiment, thespatial filtering can narrow the focus of the analysis to a reservoirsection and also allow a reduction in data typically of the order of tentimes, thereby simplifying the data analysis operations. The resultingdata set produced through the conversion of the raw optical data can bereferred to as the acoustic sample data.

This type of filtering can provide several advantages in addition to thedata set size reduction. Whether or not the acoustic data set isspatially filtered, the resulting data, for example the acoustic sampledata, used for the next step of the analysis can be indicative of anacoustic sample over a defined depth (e.g., the entire length of theoptical fiber, some portion thereof, or a point source in the wellbore114). In some embodiments, the acoustic data set can comprise aplurality of acoustic samples resulting from the spatial filter toprovide data over a number of depth ranges. In some embodiments, theacoustic sample may contain acoustic data over a depth range sufficientto capture multiple points of interest. In some embodiments, theacoustic sample data contains information over the entire frequencyrange at the depth represented by the sample. This is to say that thevarious filtering steps, including the spatial filtering, do not removethe frequency information from the acoustic sample data.

The processor 168 can be further configured to perform Discrete Fouriertransformations (DFT) or a short time Fourier transform (STFT) of theacoustic variant time domain data measured at each depth section alongthe fiber or a section thereof to spectrally check the conformance ofthe acoustic sample data to one or more acoustic signatures. Thespectral conformance check can be used to determine if the expectedsignature of an event is present in the acoustic sample data. Spectralfeature extraction through time and space can be used to determine thespectral conformance and determine if an acoustic signature (e.g., asand ingress fingerprint, gas influx, hydraulic fracturing signature,etc.) is present in the acoustic sample. Within this process, variousfrequency domain features can be calculated for the acoustic sampledata.

The use of the frequency domain features to identify one or more eventshas a number of features. First, the use of the frequency domainfeatures results in significant data reduction relative to the raw DASdata stream. Thus, a number of frequency domain features can becalculated to allow for event identification while the remaining datacan be discarded or otherwise stored, while the remaining analysis canperformed using the frequency domain features. Even when the raw DASdata is stored, the remaining processing power is significantly reducedthrough the use of the frequency domain features rather than the rawacoustic data itself. Further, the use of the frequency domain featuresprovides a concise, quantitative measure of the spectral character oracoustic signature of specific sounds pertinent to downhole fluidsurveillance and other applications that may directly be used forreal-time, application-specific signal processing.

While a number of frequency domain features can be determined for theacoustic sample data, not every frequency domain feature may be used inthe characterization of each acoustic signature. The frequency domainfeatures represent specific properties or characteristics of theacoustic signals. There are a number of factors that can affect thefrequency domain feature selection for each event. For example, a chosendescriptor should remain relatively unaffected by the interferinginfluences from the environment such as interfering noise from theelectronics/optics, concurrent acoustic sounds, distortions in thetransmission channel, and the like. In general,electronic/instrumentation noise is present in the acoustic signalscaptured on the DAS or any other electronic gauge, and it is usually anunwanted component that interferes with the signal. Thermal noise isintroduced during capturing and processing of signals by analoguedevices that form a part of the instrumentation (e.g., electronicamplifiers and other analog circuitry). This is primarily due to thermalmotion of charge carriers. In digital systems additional noise may beintroduced through sampling and quantization. The frequency domainfeatures should avoid any interference from these sources.

As a further consideration in selecting the frequency domain feature(s)for an event, the dimensionality of the frequency domain feature shouldbe compact. A compact representation is desired to decrease thecomputational complexity of subsequent calculations. The frequencydomain feature should also have discriminant power. For example, fordifferent types of audio signals, the selected set of descriptors shouldprovide altogether different values. A measure for the discriminantpower of a feature is the variance of the resulting feature vectors fora set of relevant input signals. Given different classes of similarsignals, a discriminatory descriptor should have low variance insideeach class and high variance over different classes. The frequencydomain feature should also be able to completely cover the range ofvalues of the property it describes. As an example, the chosen set offrequency domain features should be able to completely and uniquelyidentify the signatures of each of the acoustic signals pertaining to aselected downhole surveillance application or event as described herein.Such frequency domain features can include, but are not limited to, thespectral centroid, the spectral spread, the spectral roll-off, thespectral skewness, the root mean square (RMS) band energy (or thenormalized subband energies/band energy ratios), a loudness or total RMSenergy, a spectral flux, and a spectral autocorrelation function.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fiber 162 and indicates the center of gravity of thefrequency spectrum in the acoustic sample. The spectral centroid can becalculated as the weighted mean of the frequencies present in thesignal, where the magnitudes of the frequencies present can be used astheir weights in some embodiments. The value of the spectral centroid,C_(i), of the i^(th) frame of the acoustic signal captured at a spatiallocation on the fibre, may be written as:

$\begin{matrix}{C_{i} = \frac{\sum\limits_{k = 1}^{N}{{f(k)}{X_{i}(k)}}}{\sum\limits_{k = 1}^{N}{X_{i}(k)}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

Where X_(i)(k), is the magnitude of the short time Fourier transform ofthe i^(th) frame where ‘k’ denotes the frequency coefficient or binindex, N denotes the total number of bins and f(k) denotes the centrefrequency of the bin. The computed spectral centroid may be scaled tovalue between 0 and 1. Higher spectral centroids typically indicate thepresence of higher frequency acoustics and help provide an immediateindication of the presence of high frequency noise. The calculatedspectral centroid can be compared to a spectral centroid threshold orrange for a given event, and when the spectral centroid meets or exceedsthe threshold, the event of interest may be present.

The discussion below relating to calculating the spectral centroid isbased on calculating the spectral centroid of a sample data setcomprising optical data produced by the DAS system. In this case, whenassessing whether a sample data set comprises a high frequencycomponent, the calculated spectral centroid should be equal to orgreater than a spectral centroid threshold. However, if, as discussedabove, the sample data set comprises a derivative of the optical data,the calculated spectral centroid should be equal to or less than thespectral centroid threshold.

The absolute magnitudes of the computed spectral centroids can be scaledto read a value between zero and one. The turbulent noise generated byother sources such as fluid flow and inflow may typically be in thelower frequencies (e.g., under about 100 Hz) and the centroidcomputation can produce lower values, for example, around or under 0.1post rescaling. The introduction of sand can trigger broader frequenciesof sounds (e.g., a broad band response) that can extend in spectralcontent to higher frequencies (e.g., up to and beyond 5,000 Hz). Thiscan produce centroids of higher values (e.g., between about 0.2 andabout 0.7, or between about 0.3 and about 0.5), and the magnitude ofchange would remain fairly independent of the overall concentration ofsanding assuming there is a good signal to noise ratio in themeasurement assuming a traditional electronic noise floor (e.g., whitenoise with imposed flicker noise at lower frequencies). It couldhowever, depend on the size of sand particles impinging on the pipe.

The spectral spread can also be determined for the acoustic sample. Thespectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, S_(i), one has to take thedeviation of the spectrum from the computed centroid as per thefollowing equation (all other terms defined above):

$\begin{matrix}{S_{i} = \sqrt{\frac{\sum\limits_{k = 1}^{N}{\left( {{f(k)} - C_{i}} \right)^{2}{X_{i}(k)}}}{\sum\limits_{k = 1}^{N}{X_{i}(k)}}}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

Lower values of the spectral spread correspond to signals whose spectraare tightly concentrated around the spectral centroid. Higher valuesrepresent a wider spread of the spectral magnitudes and provide anindication of the presence of a broad band spectral response. Thecalculated spectral spread can be compared to a spectral spreadthreshold or range, and when the spectral spread meets exceeds thethreshold or falls within the range, the event of interest may bepresent. As in the case of the spectral centroid, the magnitude ofspectral spread would remain fairly independent of the overallconcentration of sanding for a sand ingress event assuming there is agood signal to noise ratio in the measurement. It can however, depend onthe size and shape of the sand particles impinging on the pipe.

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{\sum\limits_{k = 1}^{y}{{X_{i}(k)}}} = {\frac{c}{100}{\sum\limits_{k = 1}^{N}{{X_{i}(k)}}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

Where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain. (e.g., between gas influxand fluid flow, etc.)

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asubband energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the subbandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1. In some embodiment, selected frequency ranges for a signalwith a 5,000 Hz Nyquist acquisition bandwidth can include: a first binwith a frequency range between 0 Hz and 20 Hz, a second bin with afrequency range between 20 Hz and 40 Hz, a third bin with a frequencyrange between 40 Hz and 80 Hz, a fourth bin with a frequency rangebetween 80 Hz and 160 Hz, a fifth bin with a frequency range between 160Hz and 320 Hz, a sixth bin with a frequency range between 320 Hz and 640Hz, a seventh bin with a frequency range between 640 Hz and 1280 Hz, aneighth bin with a frequency range between 1280 Hz and 2500 Hz, and aninth bin with a frequency range between 2500 Hz and 5000 Hz. Whilecertain frequency ranges for each bin are listed herein, they are usedas examples only, and other values in the same or a different number offrequency range bins can also be used. In some embodiments, the RMS bandenergies may also be expressed as a ratiometric measure by computing theratio of the RMS signal energy within the defined frequency binsrelative to the total RMS energy across the acquisition (Nyquist)bandwidth. This may help to reduce or remove the dependencies on thenoise and any momentary variations in the broadband sound.

The total RMS energy of the acoustic waveform calculated in the timedomain can indicate the loudness of the acoustic signal. In someembodiments, the total RMS energy can also be extracted from thetemporal domain after filing the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broadbanded signals (e.g., such asthose caused by sand ingress). For tonal signals, the spectral flatnesscan be close to 0 and for broader band signals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g.: noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for anomaly detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features, can be used to provide an acoustic signaturefor a downhole event. In an embodiment, a selected set ofcharacteristics can be used to provide the acoustic signature for eachevent, and/or all of the frequency domain features that are calculatedcan be used as a group in characterizing the acoustic signature for anevent. The specific values for the frequency domain features that arecalculated can vary depending on the specific attributes of the acousticsignal acquisition system, such that the absolute value of eachfrequency domain feature can change between systems. In someembodiments, the frequency domain features can be calculated for eachevent based on the system being used to capture the acoustic signaland/or the differences between systems can be taken into account indetermining the frequency domain feature values for each signaturebetween the systems used to determine the values and the systems used tocapture the acoustic signal being evaluated.

FIG. 7 illustrates a number of different events on a chart of acousticpower versus frequency to demonstrate the differences in signatures. Asshown, the event signatures for background instrument noise, gas leaks,gas influx into the wellbore, sand ingress or influx, sand transportwithin a tubular, a self-induced hydraulic fracture, and flow behind acasing are illustrated. A plurality of frequency domain features can beused to characterize each type of event. In an embodiment, at least two,alternatively at least three, alternatively at least four, alternativelyat least five, alternatively at least six, alternatively at least seven,or alternatively at least eight different frequency domain features.While FIG. 7 only displays acoustic power, the relative frequenciespresent are illustrated for exemplary purposes to demonstrate theuniqueness of the acoustic signal result from different events, whichcan be characterized using a plurality of frequency domain features.

In an embodiment, an event comprising gas leaking from the formationinto the wellbore can be characterized by an acoustic signatureincluding a spectral centroid in a lower frequency range (e.g., in arange of about 0 Hz to about 500 Hz), with a relatively high normalizedspectral centroid value. The spectral spread may be relative small asthe expected signal may not be a broadband signal. In addition, the RMSband energy would be expected in the bins representative of frequenciesup to about 500 Hz, while the bins representative of frequencies aboveabout 500 Hz would have no RMS band energies (or subband energy ratios)or a significantly reduced RMS band energy relative to the binsrepresentative of the frequencies between 0 Hz and about 500 Hz. Inaddition, the RMS band energy representative of the frequency range ofabout 300 Hz to about 500 Hz may demonstrate the largest RMS band energy(or subband energy ratio) as related to the bins representative of theother frequency ranges. Additional frequency domain features can also bedetermined for a gas leak event and can be used as part of a gas leaksignature.

An event comprising gas influx from the formation into the wellbore canbe characterized by an acoustic signature including a spectral centroidwithin a lower frequency range (e.g., in a range of about 0 Hz to about500 Hz). The spectral spread may be relative small as the expectedsignal may not be a broadband signal. In addition, the RMS band energywould be expected in the bins representative of frequencies up to about500 Hz, while the bins representative of frequencies above about 500 Hzwould have no RMS band energies or a significantly reduced RMS bandenergy relative to the bins representative of the frequencies between 0Hz and about 500 Hz. In addition, the RMS band energy representative ofthe frequency range of about 0 Hz to about 50 Hz may demonstrate thelargest RMS band energy as related to the bins representative of theother frequency ranges. Additional frequency domain features can also bedetermined for a gas influx event and can be used as part of a gasinflux signature.

An event comprising sand ingress can be characterized by an acousticsignature including a spectral centroid above about 500 Hz. The spectralspread may be relative large as the expected signal should be abroadband signal. In addition, the RMS band energy in the binsrepresentative of frequencies above 500 Hz would be expected to havevalues above zero, thereby providing an indication of the presence ofbroadband frequencies. Additional frequency domain features can also bedetermined for a sand ingress event and can be used as part of a sandingress signature.

An event comprising a high rate of fluid flow from the formation intothe wellbore and/or within the completion assembly can be characterizedby an acoustic signature including a spectral centroid at a lowerfrequency range (e.g., within a range of 0 Hz to about 50 Hz). Thespectral spread may be relative small as the expected signal may not bea broadband signal. In addition, the RMS band energy would be expectedin the bins representative of frequencies up to about 50 Hz, while thebins representative of frequencies above about 50 Hz would have no RMSband energies or a significantly reduced RMS band energy relative to thebins representative of the frequencies between 0 Hz and about 50 Hz.Additional frequency domain features can also be determined for a highrate fluid flow event and can be used as part of a high rate fluid flowsignature.

An event comprising in-well sand transport and or the movement of a sandslug can be characterized by an acoustic signature including a spectralcentroid within a low frequency range (e.g., in arange of 0 Hz to about20 Hz). The spectral spread may be relative small as the expected signalmay not be a broadband signal. In addition, the RMS band energy would beexpected in the bins representative of frequencies up to about 20 Hz,while the bins representative of frequencies above about 20 Hz wouldhave no RMS band energies or a significantly reduced RMS band energyrelative to the bins representative of the frequencies between 0 Hz andabout 20 Hz. In addition, the RMS energy in the bins representative ofthe frequencies between 0 Hz and about 20 Hz would have an increasedenergy or power level relative to the power or energy of the fluid flownoise. The spectral roll-off may also occur at about 50 Hz. Additionalfrequency domain features can also be determined for an in-well sandtransport event and can be used as part of an in-well sand transportsignature.

An event comprising the flow of a fluid past a restriction comprising asand plug or sand dune in the wellbore tubular or production tubing canbe characterized by an acoustic signature including a spectral centroidin a low frequency range (e.g., within a range of about 0 Hz to about 50Hz). The spectral spread may be relative small as the expected signalmay not be a broadband signal. In addition, the RMS band energy would beexpected in the bins representative of frequencies up to about 50 Hz,while the bins representative of frequencies above about 50 Hz wouldhave no RMS band energies or a significantly reduced RMS band energyrelative to the bins representative of the frequencies between 0 Hz andabout 50 Hz. Additional frequency domain features can also be determinedfor fluid flow past a restriction type event and can be used as part ofa fluid flow past a restriction type signature.

An event comprising fluid flow behind a casing (e.g., between the casingand the formation) can be characterized by an acoustic signatureincluding a spectral centroid within the a low frequency range (e.g., arange of about 0 Hz to about 300 Hz). The spectral spread may berelative small as the expected signal may not be a broadband signal. Inaddition, the RMS band energy would be expected in the binsrepresentative of frequencies up to about 300 Hz, while the binsrepresentative of frequencies above about 300 Hz would have little to noRMS band energies or a significantly reduced RMS band energy relative tothe bins representative of the frequencies between 0 Hz and about 300Hz. In addition, the RMS energy in the bins representative of thefrequencies between 0 Hz and about 70 Hz would have an increased energyor power level relative to RMS energy in the remaining frequency bins.Additional frequency domain features can also be determined for fluidflow behind a casing and can be used as part of a flow behind a casingsignature.

An event comprising a self-induced hydraulic fracture that could becaused by fluid movement in the near-wellbore region can becharacterized by an acoustic signature including a spectral centroidwithin a mid-frequency range (e.g., a range of about 0 Hz to about 1000Hz). The spectral spread may be relative large as the expected signalmay include a broadband signal with frequencies extending up to about5000 Hz. In addition, the RMS band energy would be expected in the binsrepresentative of frequencies up to about 1000 Hz. In addition, thespectral flux may be indicative of the fracturing event. A largespectral flux can be expected at the initiation of the fracture due tothe near instantaneous rise in spectral power during the creation of thehydraulic fracture. The spectral flux could similarly indicate the endof the event, if the event occurs for more than a single frame duringthe acoustic monitoring. Additional frequency domain features can alsobe determined for a self-induced hydraulic fracture event and can beused as part of a self-induced hydraulic fracture signature.

An event comprising a fluid leak past a downhole restriction or plug canbe characterized by an acoustic signature including a spectral centroidin a low frequency range (e.g., in a range of 0 Hz to about 500 Hz). Thespectral spread may be relative small as the expected signal may not bea broadband signal. In addition, the RMS band energy would be expectedin the bins representative of frequencies up to about 500 Hz. Additionalfrequency domain features can also be determined for a fluid leak past arestriction type event and can be used as part of a fluid leaksignature.

An event comprising a rock fracture propagation can be characterized byan acoustic signature including a spectral centroid in a high frequencyrange (e.g., in a range of 1000 Hz to about 5000 Hz). In addition, theRMS band energy would be expected in the bins representative offrequencies between about 1000 Hz and about 5000 Hz. In addition, thespectral flux may be indicative of the fracturing propagation event. Alarge spectral flux can be expected at the initiation of the fracturepropagation due to the near instantaneous rise in spectral power duringthe fracture propagation. The spectral flux could similarly indicate theend of the event, if the event occurs for more than a single frameduring the acoustic monitoring. Additional frequency domain features canalso be determined for a rock fracturing event and can be used as partof a rock fracturing signature.

While exemplary numerical ranges are provided herein, the actualnumerical results may vary depending on the data acquisition systemand/or the values can be normalized or otherwise processed to providedifferent results. As a result, the signatures for each event may havedifferent thresholds or ranges of values for each of a plurality offrequency domain features.

In order to obtain the frequency domain features, the acoustic sampledata can be converted to the frequency domain. In an embodiment, the rawoptical data may contain or represent acoustic data in the time domain.A frequency domain representation of the data can be obtained using aFourier Transform. Various algorithms can be used as known in the art.In some embodiments, a Short Time Fourier Transform technique or aDiscrete Time Fourier transform can be used. The resulting data samplemay then be represented by a range of frequencies relative to theirpower levels at which they are present. The raw optical data can betransformed into the frequency domain prior to or after the applicationof the spatial filter. In general, the acoustic sample will be in thefrequency domain in order to determine the spectral centroid and thespectral spread. In an embodiment, the processor 168 can be configuredto perform the conversion of the raw acoustic data and/or the acousticsample data from the time domain into the frequency domain. In theprocess of converting the signal to the frequency domain, the poweracross all frequencies within the acoustic sample can be analyzed. Theuse of the processor 168 to perform the transformation may provide thefrequency domain data in real time or near real time.

The processor 168 can then be used to analyze the acoustic sample datain the frequency domain to obtain one or more of the frequency domainfeatures and provide an output with the determined frequency domainfeatures for further processing. In some embodiments, the output of thefrequency domain features can include features that are not used todetermine the presence of every event.

The output of the processor with the frequency domain features for theacoustic sample data can then be used to determine the presence of oneor more events at one or more locations in the wellbore corresponding todepth intervals over which the acoustic data is acquired or filtered. Insome embodiments, the determination of the presence of one or moreevents can include comparing the frequency domain features with thefrequency domain feature thresholds or ranges in each event signature.When the frequency domain features in the acoustic sample data match oneor more of the event signatures, the event can be identified as havingoccurred during the sample data measurement period, which can be in realtime. Various outputs can be generated to display or indicate thepresence of the one or more events.

The matching of the frequency domain features to the event signaturescan be accomplished in a number of ways. In some embodiments, a directmatching of the frequency domain features to the event signaturethresholds or ranges can be performed across a plurality of frequencydomain features. In some embodiments, machine learning or evendeterministic techniques may be incorporated to allow new signals to bepatterned automatically based on the descriptors. As an example, k-meansclustering and k-nearest neighbor classification techniques may be usedto cluster the events and classify them to their nearest neighbor tooffer exploratory diagnostics/surveillance capability for variousevents, and in some instances, to identify new downhole events that donot have established event signatures. The use of learning algorithmsmay also be useful when multiple events occur simultaneously such thatthe acoustic signals stack to form the resulting acoustic sample data.In an embodiment, the frequency domain features can be used to determinethe presence of sand ingress in one or more locations in the wellbore.The determination of the spectral centroid and the spectral spread, andthe comparison with the thresholds may allow for a determination of thepresence of particles in the fluid at the selected depth in thewellbore. Since the high frequency components tend to be present at thelocation at which the sand is entering the wellbore tubular with thefluid, the locations meeting the spectral spread and spectral centroidcriteria indicate those locations at which sand ingress is occurring.This may provide information on the ingress point rather than simply alocation at which sand is present in the wellbore tubular (e.g., presentin a flowing fluid), which can occur at any point above the sand ingresslocation as the fluid flows to the surface of the wellbore 114.

As above, the spectral spread can be computed using the spectralcentroid, and so typically the spectral centroid is calculated first,followed by the spectral spread. The comparison of the spectral spreadand the spectral centroid with the corresponding threshold can occur inany order. In some embodiments, both values can be calculated, alone oralong with additional frequency domain features, and compared to thecorresponding threshold values or ranges to determine if sand ingress ispresent at the depth represented by the acoustic sample data. In otherembodiments, only one of the two properties may be determined first. Ifthe value of the spectral spread or the spectral centroid, whichever isdetermined first, is not above the corresponding threshold, the energyvalue for the depth or depth range represented by the acoustic sampledata can be set to zero, and another sample can be processed. If thevalue is greater than the corresponding threshold, then the otherproperty can be determined and compared to the corresponding threshold.If the second comparison does not result in the property exceeding thethreshold, the energy value for the depth range represented by theacoustic sample data can be set to zero. This may result in a data pointcomprising a value of zero such that a resulting log may comprise a zerovalue at the corresponding depth. Only when both properties meet orexceed the corresponding threshold is another value such as the energyor intensity value recorded on a data log for the well. The calculatedvalues for the energy or intensity can be stored in the memory 170 forthose acoustic sample data sets in depth and time meeting or exceedingthe corresponding thresholds, and a value of zero can be stored in thememory 170 for those acoustic sample data sets not meeting or exceedingone or both of the corresponding thresholds.

The other events can also be identified in a similar manner to thepresence of the sand ingress. In an embodiment, a gas leak event can becharacterized by a gas leak signature that comprises a threshold rangefor each of a plurality of spectral descriptors (e.g., the spectralspread, the spectral roll-off, the spectral skewness, the root meansquare (RMS) band energy (or the normalized sub-band energies/bandenergy ratios), a loudness or total RMS energy, a spectral flux, and aspectral autocorrelation function). The gas leak signature can beindicative of a gas leak from a formation in the wellbore through a leakpath. The processor, using the analysis application, can be configuredto compare the spectral descriptor values to the thresholds and/orranges and determine if a gas leak from the formation to the annulus inthe wellbore has occurred. The determination of the spectral descriptorvalues can be performed in any order, and the determination can be madesequentially (e.g., verifying a first frequency domain feature is withina threshold and/or range, followed by a second frequency domain feature,etc.), or in parallel using the frequency domain features in the eventsignature.

In an embodiment, gas influx into the wellbore can be characterized by agas influx signature comprising a threshold range for each of aplurality of spectral descriptors (e.g., the spectral spread, thespectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The gas influx signature can be indicative ofa gas inflow from a formation into the wellbore. The processor, usingthe analysis application, can be configured to compare the plurality ofspectral descriptor values to the thresholds and/or ranges and determineif gas influx from the formation to the annulus in the wellbore hasoccurred. The determination of the spectral descriptor values can beperformed in any order, and the determination can be made sequentially(e.g., verifying a first frequency domain feature is within a thresholdand/or range, followed by a second frequency domain feature, etc.), orin parallel using the frequency domain features in the event signature.

In an embodiment, liquid inflow into the wellbore can be characterizedby a liquid inflow signature that comprises a spectral centroidthreshold range and an RMS band energy range, and the frequency domainfeatures can include a spectral centroid and RMS band energies in aplurality of bins. The liquid inflow signature can be indicative of aliquid inflow from a formation into the wellbore. The processor, usingthe analysis application, can be configured to compare the plurality ofspectral descriptor values to the thresholds and/or ranges and determineif liquid inflow from the formation has occurred. The determination ofthe spectral descriptor values can be performed in any order, and thedetermination can be made sequentially (e.g., verifying a firstfrequency domain feature is within a threshold and/or range, followed bya second frequency domain feature, etc.), or in parallel using thefrequency domain features in the event signature.

In an embodiment, sand transport within the wellbore can becharacterized by a sand transport signature that comprises a spectralcentroid threshold range and a spectral rolloff threshold, and thefrequency domain features can include a spectral centroid and a spectralrolloff. The sand transport signature can be indicative of sand flowingwithin a carrier fluid within the wellbore. The processor, using theanalysis application, can be configured to compare the plurality ofspectral descriptor values to the thresholds and/or ranges and determineif sand transport within the wellbore has occurred. The determination ofthe spectral descriptor values can be performed in any order, and thedetermination can be made sequentially (e.g., verifying a firstfrequency domain feature is within a threshold and/or range, followed bya second frequency domain feature, etc.), or in parallel using thefrequency domain features in the event signature.

In an embodiment, fluid flow past a sand restriction can becharacterized by a sand restriction signature that comprises a spectralpower threshold range, and the frequency domain features can comprise aspectral power. The sand restriction signature can be indicative of aliquid flow past a sand restriction in a tubular within the wellbore.The processor, using the analysis application, can be configured tocompare the plurality of spectral descriptor values to the thresholdsand/or ranges and determine if fluid flow past a sand restriction hasoccurred. The determination of the spectral descriptor values can beperformed in any order, and the determination can be made sequentially(e.g., verifying a first frequency domain feature is within a thresholdand/or range, followed by a second frequency domain feature, etc.), orin parallel using the frequency domain features in the event signature.

In an embodiment, fluid flow behind a casing (e.g., fluid flow through aleak path, etc.) can be characterized by a casing fluid flow signaturethat comprises a spectral power threshold range and one or more an RMSband energy ranges, and the frequency domain features can comprise aspectral centroid and RMS band energies in a plurality of bins. Thecasing fluid flow signature can be indicative of a fluid flow between acasing and a formation. The processor, using the analysis application,can be configured to compare the plurality of spectral descriptor valuesto the thresholds and/or ranges and determine if fluid flow behind acasing has occurred. The determination of the spectral descriptor valuescan be performed in any order, and the determination can be madesequentially (e.g., verifying a first frequency domain feature is withina threshold and/or range, followed by a second frequency domain feature,etc.), or in parallel using the frequency domain features in the eventsignature.

In an embodiment, the occurrence of a self-induced hydraulic fracturecan be characterized by a self-induced hydraulic fracturing signaturethat comprises a spectral centroid threshold range and an RMS bandenergy range, and the frequency domain features can comprise a spectralcentroid and RMS band energies in a plurality of bins. The self-inducedhydraulic fracturing signature can be indicative of a formation of aself-induced fracture within a formation. The processor, using theanalysis application, can be configured to compare the plurality ofspectral descriptor values to the thresholds and/or ranges and determineif a self-induced hydraulic fracture has occurred. The determination ofthe spectral descriptor values can be performed in any order, and thedetermination can be made sequentially (e.g., verifying a firstfrequency domain feature is within a threshold and/or range, followed bya second frequency domain feature, etc.), or in parallel using thefrequency domain features in the event signature.

In an embodiment, the presence of a fluid leak can be characterized by afluid leak signature that comprises a spectral centroid threshold rangeand an RMS band energy range, and the frequency domain features cancomprise a spectral centroid and RMS band energies in a plurality ofbins. The fluid leak signature can be indicative of a liquid flow past adownhole plug within the wellbore. The processor, using the analysisapplication, can be configured to compare the plurality of spectraldescriptor values to the thresholds and/or ranges and determine if fluidflow past a restriction such as a downhole plug has occurred. Thedetermination of the spectral descriptor values can be performed in anyorder, and the determination can be made sequentially (e.g., verifying afirst frequency domain feature is within a threshold and/or range,followed by a second frequency domain feature, etc.), or in parallelusing the frequency domain features in the event signature.

In an embodiment, the occurrence of a fracture within the formation canbe characterized by a fracturing signature that comprises a spectralcentroid threshold range and an RMS band energy range, and the frequencydomain features can comprise a spectral centroid and RMS band energiesin a plurality of bins. The fracturing signature is indicative of aformation of a fracturing within a formation. The processor, using theanalysis application, can be configured to compare the plurality ofspectral descriptor values to the thresholds and/or ranges and determineif a fracture in the formation has occurred. The determination of thespectral descriptor values can be performed in any order, and thedetermination can be made sequentially (e.g., verifying a firstfrequency domain feature is within a threshold and/or range, followed bya second frequency domain feature, etc.), or in parallel using thefrequency domain features in the event signature.

In addition to detecting the presence of one or more events at a depthor location in the wellbore 114, the analysis software executing on theprocessor 168 can be used to visualize the event locations or transferthe calculated energy values over a computer network for visualizationon a remote location. In order to visualize one or more of the events,the energy or intensity of the acoustic signal can be determined at thedepth interval of interest (e.g., reservoir section where the sandingress locations are to be determined)

The intensity of the acoustic signal in the filtered data set can thenbe calculated, where the intensity can represent the energy or power inthe acoustic data. A number of power or intensity values can becalculated. In an embodiment, the root mean square (RMS) spectral energyor sub-band energy ratios across the filtered data set frequencybandwidth can be calculated at each of the identified event depthsections over a set integration time to compute an integrated data traceof the acoustic energies over all or a portion of the length of thefiber as a function of time. This computation of an event log may bedone repeatedly, such as every second, and later integrated/averaged fordiscrete time periods—for instance, at times of higher well drawdowns,to display a time-lapsed event log at various stages of the productionprocess (e.g., from baseline shut-in, from during well ramp-up, fromsteady production, from high drawdown/production rates etc.). The timeintervals may be long enough to provide suitable data, though longertimes may result in larger data sets. In an embodiment, the timeintegration may occur over a time period between about 0.1 seconds toabout 10 seconds, or between about 0.5 seconds and about a few minutesor even hours.

The resulting event log(s) computed every second can be stored in thememory 170 or transferred across a computer network, to populate anevent database. The data stored/transferred in the memory 170 caninclude any of the frequency domain features, the filtered energy dataset, and/or the RMS spectral energy through time, for one or more of thedata set depths and may be stored every second. This data can be used togenerate an integrated event log at each event depth sample point alongthe length of the optical fiber 162 along with a synchronized timestampthat indicates the times of measurement. In producing a visualizationevent log, the RMS spectral energy for depth sections that do notexhibit or match one or more event signatures can be set to zero. Thisallows those depth points or zones exhibiting or matching one or more ofthe event signatures to be easily identified.

As an example, the analysis software executing on the processor 168 canbe used to visualize sand ingress locations or transfer the calculatedenergy values over a computer network for visualization on a remotelocation. In order to visualize the sand ingress, the energy orintensity of the acoustic signal, or at least the high frequency portionof the acoustic signal, can be determined at the depth interval ofinterest (e.g., reservoir section where the sand ingress locations areto be determined)

When the spectral descriptors have values above the correspondingthresholds in the event signature, the acoustic sample data can befiltered to obtain the sand ingress acoustic data. In some embodiments,only the acoustic sample data meeting or exceeding the correspondingthresholds may be further analyzed, and the remaining acoustic sampledata can have the value set to zero. The acoustic sample data setsmeeting or exceeding the corresponding thresholds can be filtered with ahigh frequency filter. In an embodiment, the acoustic sample data setsmeeting or exceeding the corresponding thresholds can be filtered with ahigh frequency filter to remove the frequencies below about 0.5 kHz,below about 1 kHz, below about 1.5 kHz, or below about 2 kHz. The upperfrequency range may be less than about 10 kHz, less than about 7 kHz,less than about 6 kHz, or less than about 5 kHz, where the filterbandwidth can have a frequency range between any of the lower values andany of the upper values. In an embodiment, the acoustic sample can befiltered to produce a filtered data set comprising the frequenciesbetween about 0.5 kHz and about 10 kHz, or between about 2 kHz and about5 kHz from the acoustic sample. The filtered data set allows the broadband acoustic energy in the higher frequencies to be isolated, andthereby allow the sand ingress acoustics to be distinguished from thegeneral, low frequency fluid flow noise captured by the acoustic sensorresulting from fluid flow and mechanical sources of acoustic signals.

The intensity of the acoustic signal in the filtered data set can thenbe calculated, where the intensity can represent the energy or power inthe acoustic data. In an embodiment, the root mean square (RMS) spectralenergy across the filtered data set frequency bandwidth can becalculated at each of the identified sanding depth sections over a setintegration time to compute an integrated data trace of sand ingressenergies over all or a portion of the length of the fiber as a functionof time. This computation of a ‘sand ingress log’ may be donerepeatedly, such as every second, and later integrated/averaged fordiscrete time periods—for instance, at times of higher well drawdowns,to display a time-lapsed sand ingress log at various stages of theproduction process (e.g., from baseline shut-in, from during wellramp-up, from steady production, from high drawdown/production ratesetc.). The time intervals may be long enough to provide suitable data,though longer times may result in larger data sets. In an embodiment,the time integration may occur over a time period between about 0.1seconds to about 10 seconds, or between about 0.5 seconds and about afew minutes or even hours.

Sand logs computed every second can be stored in the memory 170 ortransferred across a computer network, to populate an event database.The data stored/transferred in the memory 170 can include the measuredspectral centroid, the measured spectral spread, the filtered energydata set, and/or the RMS spectral energy through time, for one or moreof the data set depths and may be stored every second. This data can beused to generate an integrated high frequency sanding energy log at eachevent depth sample point along the length of the optical fiber 162 alongwith a synchronized timestamp that indicates the times of measurement.

In producing a visualization sanding log, the RMS spectral energy fordepth sections that do not exhibit the spectral conformance can be setto zero. This allows those depth points or zones having spectralcentroids and spectral spreads greater than the thresholds to be easilyobserved. FIG. 8 represents an example of an embodiment of a sanding logshowing RMS spectral energy against depth. The figure illustrates thelocations having sand ingress locations as peaks in the total RMSspectral energy. In an embodiment, the band filtered spectral energydata can be visualized alongside or on a well completion schematic or anopen hole petrophysical log indicating zones of sanding at theintegration time intervals to allow for easy identification relative tothe equipment and producing zones in a wellbore. The sand ingress logcan also be visualized as a 3D plot with the RMS spectral energy alongthe vertical axis (x axis); sample point depth along the y axis and timealong the z axis. This embodiment provides a DAS sand log that can allowfor a visualization of the zonal sand contributions in near real time.In some instances, the sanding events may not be continuous, and thetime-based log may allow for the visualization of the sand ingress in atime dependent fashion.

The RMS spectral energy and its visualization on the sand log cantherefore be used to identify the relative contribution of the sandingingress at different points along the wellbore. For example, it may bepossible to determine which zone is contributing the greatest proportionof the sand ingress, which zone contributes the second greatest portionof the sand ingress, and so on.

In some embodiments, a qualitative determination of the amount of sandentering the wellbore can occur at one or more locations. In order todetermine qualitative amount of sand entering the wellbore, theprocessor can be configured to determine an integrated (cumulative)magnitude and quality factor and/or width of one or more of the peaks inthe power data representing the intensity or power relative to a depthover a discrete time period. The quality factor or the half powerbandwidth represents the sharpness of the peak. The quality factor, inaddition to the magnitude of peaks at each sanding zones, provides aqualitative indication of the concentration of sand where lowconcentrations produce low amplitudes with high quality factors, highconcentrations of sanding produce large magnitude peaks with arelatively poorer quality factor, and intermediate sand concentrationsproduce peaks of large magnitudes with relatively high quality factors.By determining the quality factor, width of the peaks, and/or relativemagnitude of the peaks, the relative amount of sand ingress at variouszones can be determined. For example, the qualitative sand ingressamount may be classified based on the quality factor and/or width of thepeaks, using terms such as “high; medium; low”, “severe; moderate; low”or “3; 2; 1” or similar. This qualitative sand intensity estimate acrosseach of the sanding zones may also be proportionally translated into asand allocation in pptb (parts per thousand barrels) by correlating thedata to the sand measured on the surface in cases where the well isoperated with enough rate to lift sand produced to surface. This surfacesand measurement may be done by taking lab samples/through the use ofother quantitative surface sand detection systems. This information maybe useful in planning for a remediation action to reduce the amount ofsand entering the wellbore.

The data output by the system may generally indicate one or more sandinglocations or depths, and optionally, a relative amount of sand ingressbetween the identified locations or depths and/or a qualitativeindicator of sand entering the wellbore at a location. If sand ingressis observed in the produced fluid (as determined by methods such assurface sand detectors, visual observation, etc.), but the locationand/or amount of the sand ingress cannot be identified with sufficientclarity using the methods described herein, various actions can be takenin order to obtain a better visualization of the acoustic data. In anembodiment, the production rate can be temporarily increased. Theresulting data analysis can be performed on the data during theincreased production period. In general, an increased fluid flow rateinto the wellbore may be expected to increase the acoustic signalintensity at the sand ingress locations. This may allow a signal tonoise ratio to be improved in order to more clearly identify sandingress at one or more locations by, for example, providing for anincreased signal strength to allow the spectral conformance to bedetermined. The sand energies can also be more clearly calculated basedon the increased signal outputs. Once the zones of interest areidentified, the production levels can be adjusted based on the sandingress locations and amounts. Any changes in sand production amountsover time can be monitored using the techniques described herein and theoperating conditions can be adjusted accordingly (e.g., dynamicallyadjusted, automatically adjusted, manually adjusted, etc.).

In some embodiments, the change in the production rate can be used todetermine a production rate correlation with the sand ingress locationsand inflow rates at one or more points along the wellbore. In general,decreasing the production rate may be expected to reduce the sandingress rates. By determining production rate correlations with the sandingress rates, the production rate from the well and/or one or morezones can be adjusted to reduce the sand ingress rate at the identifiedlocations. For example, an adjustable production sleeve or choke can bealtered to adjust specific sand ingress rates in one or more productionzones. If none of the production zones are adjustable, various workoverprocedures can be used to alter the production from specific zones. Forexample, various intake sleeves can be blocked off, zonal isolationdevices can be used to block off production from certain zones, and/orsome other operations can be carried out to reduce the amount of sandingress (e.g., consolidation procedures, etc.).

The same analysis procedure can be used with any of the event signaturesdescribed herein. For example, the presence of one or more events can bedetermined. In some embodiments, the location and or discriminationbetween events may not be clear. One or more characteristics of thewellbore can then be changed to allow a second measurement of theacoustic signal to occur. For example, the production rate can bechanged, the pressures can be changed, one or more zones can be shut-in,or any other suitable production change. For example, the productionrate can be temporarily increased. The resulting data analysis can beperformed on the data during the increased production period. Ingeneral, an increased fluid flow rate into the wellbore may be expectedto increase the acoustic signal intensity at certain event locationssuch as a gas influx location, a sand ingress location, a fluid inflowlocation, or the like. Similarly, such a change may not change theintensity in other types of events such as fluid leaks, hydraulicfractures, and similar events. This may allow a signal to noise ratio tobe improved in order to more clearly identify one event relative toanother at one or more locations by, for example, providing for anincreased signal strength to allow the event signatures to be comparedto the resulting acoustic signal. The event energies can also be moreclearly calculated based on the increased signal outputs. Once the zonesof interest are identified, the production levels can be adjusted basedon the event locations and amounts. Any changes in the presence of theevents over time can be monitored using the techniques described hereinand the operating conditions can be adjusted accordingly (e.g.,dynamically adjusted, automatically adjusted, manually adjusted, etc.).While the data analysis has been described above with respect to thesystem 100, methods of identifying events within the wellbore (e.g.,sand ingress locations along the length of a wellbore, hydraulicfractures, gas influx, etc.) can also be carried out using any suitablesystem. For example, the system of FIG. 1 can be used to carry out theidentification method, a separate system at a different time and/orlocation can be used with acoustic data to perform the eventidentification method, and/or the method can be performed using acousticdata obtained from a different type of acoustic sensor where the data isobtained in an electronic form useable with a device capable ofperforming the method.

Additional data processing techniques can also be used to detect eventsin the wellbore. In some embodiments, the processor 168 can execute aprogram, which can configure the processor 168 to filter the acousticdata set spatially and spectrally to provide frequency band extracted(FBE) acoustic data over multiple frequency bands. This can be similarto the frequency bands described with respect to the RMS energies. Theacoustic data set can be pre-processed and then frequency filtered in tomultiple frequency bands at given intervals such as every second of dataacquisition. The multiple frequency bands can include various ranges. Asan example, the multiple frequency bands can include a first band fromabout 5 Hz to about 50 Hz; a second band from about 50 Hz to about 100Hz; a third band from about 100 Hz to about 500 Hz; a fourth band fromabout 500 Hz to about 2000 Hz; a fifth band from about 2000 Hz to about5000 Hz, and so on along the length of the fiber or a selected portionthereof, though other ranges for the frequency bands can also be used.).

The resulting FBE data can then be cross compared to identify zones withevent signature corresponding to the FBE data. For example, the acousticamplitudes in each of the multiple frequency bands can be compared todetermine depths with response relative to a baseline acoustic signal.The baseline acoustic signal can be taken as the measured acousticscaptured when the well is shut-in (e.g., without producing a fluid). Insome embodiments, the baseline acoustic signal can comprise a timeaveraged acoustic signal over one or more portions of the wellbore. Thetime period for considering the average may be taken as long enough toavoid the potential of an event over the entire average. Any comparisonof an acoustic signal comprising an event to the time average shouldthen indicate an increased signal in at least one frequency rangescorresponding to the event frequency ranges of interest.

Using sand ingress detection as an example, additional data processingtechniques can also be used to detect sand ingress locations. Theresulting FBE data can then be cross compared to identify zones with thesand ingress signature to compute a representative sand log. Forexample, the acoustic amplitudes in each of the multiple frequency bandscan be compared to determine depths with broadband response (e.g., zoneswhere a response in all of the bands is observed) relative to a baselineacoustic signal. The baseline acoustic signal can be taken as themeasured acoustics captured when the well is shut-in (e.g., withoutproducing a fluid). In some embodiments, the baseline acoustic signalcan comprise a time averaged acoustic signal over one or more portionsof the wellbore. Any comparison of an acoustic signal comprising sandinflow to the time average should then indicate an increased signal inat least one broadband frequency range (e.g., in a frequency rangehaving a frequency greater than 0.5 kHz such as 0.5 kHz to about 5 kHz).The zones having a broadband response can then be identified, and theacoustic RMS energies in the higher frequencies in the identified zonescan be populated as the sand noise intensity as done in the previousdescribed processing workflow. In addition to the systems describedherein, various methods of determining the presence of one or moreevents can also be carried out. The methods can be performed using anyof the systems described herein, or any other suitable systems. In anembodiment, a method of detecting an event within a wellbore can includeobtaining a sample data set. The sample data set can be a sample of anacoustic signal originating within a wellbore comprising a fluid, and berepresentative of the acoustic signal across a frequency spectrum. Aplurality of frequency domain features of the sample data set can bedetermined, and the plurality of spectral characteristics can becompared with corresponding threshold and/or ranges an event signature.When the plurality of frequency domain features match the eventsignature, the presence of the event within the wellbore can bedetermined based on the determination that that at least one spectralcharacteristic matches the event signature.

The event signature can include any of those described herein such as agas leak from a subterranean formation into an annulus in the wellbore,a gas inflow from the subterranean formation into the wellbore, sandingress into the wellbore, a liquid inflow into the wellbore, sandtransport within a tubular in the wellbore, fluid flow past a sand plugin a tubular in the wellbore, fluid flow behind a casing, a self-inducedhydraulic fracture within the subterranean formation, a fluid leak pasta downhole seal, or a rock fracture propagation event.

In an embodiment, the method can be used to determine the presence of asand inflow into a wellbore using a sand ingress signature. The sampledata set can be analyzed to determine that the sample data set comprisesacoustic frequencies greater than about 0.5 kHz, and the spectralcharacteristic can include a spectral centroid of the sample data setand a spectral spread of the sample data set. The sand ingress signaturecan include a spectral centroid threshold and a spectral spreadthreshold. A determination that the at least one spectral characteristicmatches the event signature can be made by determining that the spectralcentroid is greater than a spectral centroid threshold, determining thatthe spectral spread is greater than a spectral spread threshold, anddetermining a presence of sand inflow into the wellbore based ondetermining that the at least one spectral characteristic matches theevent signature.

In an embodiment, the method can be used to determine the presence of agas leak using a gas leak signature that is indicative of a gas leakfrom a formation through a leak path in the wellbore. The frequencydomain features can include a plurality of the frequency domain featuresdescribed herein (e.g., the spectral spread, the spectral roll-off, thespectral skewness, the root mean square (RMS) band energy (or thenormalized sub-band energies/band energy ratios), a loudness or totalRMS energy, a spectral flux, and/or a spectral autocorrelationfunction). The determination of the presence of the gas leak can be madeby comparing the plurality of frequency domain features to thethresholds and/or ranges and determining if a gas leak from theformation to the annulus in the wellbore has occurred.

In an embodiment, the method can be used to determine the presence ofgas influx into the wellbore using a gas influx signature that comprisesthresholds and/or ranges for a plurality of frequency domain features.The frequency domain features can include a plurality of the frequencydomain features described herein (e.g., the spectral spread, thespectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The determination of the presence of the gasleak can be made by comparing a plurality of frequency domain featurevalues in an acoustic sample to the thresholds and/or ranges anddetermining if a gas leak from the formation to the annulus in thewellbore has occurred.

In an embodiment, the method can be used to determine the presence ofliquid inflow into the wellbore using a liquid inflow signature thatcomprises thresholds and/or ranges for a plurality of frequency domainfeatures. The frequency domain features can include a plurality of thefrequency domain features described herein (e.g., the spectral spread,the spectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The liquid inflow signature can be indicativeof a liquid inflow from a formation into the wellbore. The determinationof the presence of the liquid inflow can be made by comparing aplurality of frequency domain feature values in an acoustic sample tothe thresholds and/or ranges and determining if the liquid inflow hasoccurred.

In an embodiment, the method can be used to determine the presence ofsand being transported within the wellbore in a carrier fluid using asand transport signature that comprises thresholds and/or ranges for aplurality of frequency domain features. The frequency domain featurescan include a plurality of the frequency domain features describedherein (e.g., the spectral spread, the spectral roll-off, the spectralskewness, the root mean square (RMS) band energy (or the normalizedsub-band energies/band energy ratios), a loudness or total RMS energy, aspectral flux, and/or a spectral autocorrelation function). The sandtransport signature can be indicative of a sand being transported withina tubular. The determination of the presence of the sand transport canbe made by comparing a plurality of frequency domain feature values inan acoustic sample to the thresholds and/or ranges and determining ifthe sand transport has occurred.

In an embodiment, the method can be used to determine the presence offluid flowing past a sand restriction. using a sand restrictionsignature comprising thresholds and/or ranges for a plurality offrequency domain features. The frequency domain features can include aplurality of the frequency domain features described herein (e.g., thespectral spread, the spectral roll-off, the spectral skewness, the rootmean square (RMS) band energy (or the normalized sub-band energies/bandenergy ratios), a loudness or total RMS energy, a spectral flux, and/ora spectral autocorrelation function). The determination of the presenceof the sand restriction can be made by comparing a plurality offrequency domain feature values in an acoustic sample to the thresholdsand/or ranges and determining if the sand restriction is present.

In an embodiment, the method can be used to determine the presence offluid flowing between a casing and the formation using a casing fluidflow signature that comprises thresholds and/or ranges for a pluralityof frequency domain features. The frequency domain features can includea plurality of the frequency domain features described herein (e.g., thespectral spread, the spectral roll-off, the spectral skewness, the rootmean square (RMS) band energy (or the normalized sub-band energies/bandenergy ratios), a loudness or total RMS energy, a spectral flux, and/ora spectral autocorrelation function). The liquid inflow signature can beindicative of a liquid inflow from a formation into the wellbore. Thedetermination of the presence of the fluid flow behind a casing can bemade by comparing a plurality of frequency domain feature values in anacoustic sample to the thresholds and/or ranges and determining if thefluid flow behind the casing has occurred.

In an embodiment, the method can be used to determine the occurrence ofa self-induced hydraulic fracture within the formation using aself-induced hydraulic fracturing signature that comprises thresholdsand/or ranges for a plurality of frequency domain features. Thefrequency domain features can include a plurality of the frequencydomain features described herein (e.g., the spectral spread, thespectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The self-induced hydraulic fracturingsignature can be indicative of a formation of a self-induced fracturewithin a formation. The determination of the presence of theself-induced hydraulic fracture can be made by comparing a plurality offrequency domain feature values in an acoustic sample to the thresholdsand/or ranges and determining if the self-induced hydraulic fracture hasoccurred.

In an embodiment, the method can be used to determine the presence offluid leaking past a restriction using a fluid leak signature thatcomprises thresholds and/or ranges for a plurality of frequency domainfeatures. The frequency domain features can include a plurality of thefrequency domain features described herein (e.g., the spectral spread,the spectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The determination of the presence of thefluid leaking past the restriction can be made by comparing a pluralityof frequency domain feature values in an acoustic sample to thethresholds and/or ranges and determining if the fluid leak past therestriction has occurred.

In an embodiment, the method can be used to determine the occurrence ofa fracture within the formation using a fracturing signature thatcomprises thresholds and/or ranges for a plurality of frequency domainfeatures. The frequency domain features can include a plurality of thefrequency domain features described herein (e.g., the spectral spread,the spectral roll-off, the spectral skewness, the root mean square (RMS)band energy (or the normalized sub-band energies/band energy ratios), aloudness or total RMS energy, a spectral flux, and/or a spectralautocorrelation function). The determination of the presence of thefracture can be made by comparing a plurality of frequency domainfeature values in an acoustic sample to the thresholds and/or ranges anddetermining if the fracture has occurred.

In addition to other methods described herein, a method of determiningthe presence of sand ingress within a wellbore can start with obtainingan acoustic signal from within a wellbore. The wellbore can comprise afluid serving as a carrier fluid for the sand. In some embodiments, thefluid can produced from the well during the time the acoustic signal isobtained so that the fluid carrying the sand is flowing within thewellbore or wellbore tubular serving as the production tubing, and/orthe fluid can be flowing from the formation into the wellbore.

The acoustic signal can include data for all of the wellbore or only aportion of the wellbore. An acoustic sample data set can be obtainedfrom the acoustic signal. In an embodiment, the sample data set mayrepresent a portion of the acoustic signal for a defined depth range orpoint. In some embodiments, the acoustic signal can be obtained in thetime domain. For example, the acoustic signal may be in the form of anacoustic amplitude relative to a collection time. The sample data setmay also be in the time domain and be converted into the frequencydomain using a suitable transform such as a Fourier transform. In someembodiments, the sample data set can be obtained in the frequency domainsuch that the acoustic signal can be converted prior to obtaining thesample data set. While the sample data set can be obtained using any ofthe methods described herein, the sample data set can also be obtainedby receiving it from another device. For example, a separate extractionor processing step can be used to prepare one or more sample data setsand transmit them for separate processing using any of the processingmethods or systems disclosed herein.

The spectral conformance of the sample data set can then be obtainedusing various conformance checks. In an embodiment, a spectral centroidof the sample data set can be determined and compared to a spectralcentroid threshold. Similarly, a spectral spread of the sample data setcan be determined and compared to a spectral spread threshold. If eitherthe spectral centroid or the spectral spread does not exceed thecorresponding threshold, sand ingress may not be occurring at the depthrepresented by the sample data set. In some embodiments, the spectralspread and the spectral centroid can be determined and compared to theapplicable threshold serially, and the failure of either one to meet thecorresponding threshold may stop the process such that the otherspectral property may not be determined. When both the spectral spreadand the spectral centroid meet or exceed the applicable threshold, thepresence of sand in the fluid (e.g., in the fluid entering the wellbore)can be determined to be occurring.

The overall method and corresponding steps are schematically illustratedas a flowchart show in FIG. 9. As shown in FIG. 9, an embodiment of amethod 600 for detecting sand ingress into a wellbore can begin with anacoustic sensor such as a DAS system obtaining, detecting, or receivingan acoustic signal, for example, from an optical fiber 162, as shown instep 602. The acoustic signal can be generated within the wellbore asdescribed herein. The raw optical data from the acoustic sensor can bereceived and generated by the sensor to produce the acoustic signal, asshown in step 604. The data rate generated by various acoustic sensorssuch as the DAS system can be large. For example, the DAS system maygenerate data on the order of 0.5 to about 2 terabytes per hour. Thisraw data can optionally be stored in a memory in step 603.

The raw data can then be optionally pre-processed in step 605. As shownin FIG. 9, the pre-processing can be performed using a number ofoptional steps. For example, a spatial sample point filter can beapplied in step 606. This filter uses a filter to obtain a portion ofthe acoustic signal corresponding to a desired depth in the wellbore.Since the time the light pulse sent into the optical fiber returns asbackscattered light can correspond to the travel distance, and thereforedepth in the wellbore, the acoustic data can be processed to obtain asample indicative of the desired depth or depth range. This may allow aspecific location within the wellbore to be isolated for furtheranalysis. The pre-processing step may also include removal of spuriousback reflection type noises at specific depths through spatial medianfiltering or spatial averaging techniques.

In step 607, the filtered data can be transformed from the time domaininto the frequency domain using a transform such as a Fourier transform(e.g., a Short time Fourier Transform or through Discrete Fouriertransformation). By transforming the data after applying the spatialfilter, the amount of data processed in the transform can be reduced.

In step 608, a noise normalization routine can be performed on the datato improve the signal quality. This step can vary depending on the typeof acquisition device used as well as the configuration of the lightsource, the sensor, and the other processing routines. While shown in aspecific order in FIG. 9, the order of the steps within thepre-processing routines can be varied, and any order of the steps 606,607, 608 can be used. The resulting sample data set may have a reduceddata size compared to the raw data set. In an embodiment, a ratio of thesample data file size after the pre-processing to the raw data file sizebefore the pre-processing can be between about 0.05 and about 0.5, oraround 0.1, or less if the data is spatially/temporally averaged.

After the acoustic signal is pre-processed, the sample data set can beused in a spectral conformance check process or routine in step 610. Thespectral conformance process can include first determining at least oneof the spectral centroid or the spectral spread. As shown in FIG. 9, thefirst step in the spectral conformance check can include determining thespectral centroid of the sample data set. The spectral centroid can thenbe compared against a spectral centroid threshold in the comparison step614. When the spectral centroid meets or is greater than the spectralcentroid threshold, the process can proceed to the next comparison step618. In step 618, a spectral spread for the sample data set can bedetermined. The spectral spread can then be compared to a spectralspread threshold in step 618. When the spectral spread meets or isgreater than the spectral spread threshold, the process can proceed tothe next step 622. When the sample data set has both a spectral spreadand a spectral centroid above the corresponding threshold, it can bedetermined that the acoustic data at the depth represented by the sampledata set represents the ingress of sand. This can include the presenceof sand in the fluid at the depth location as well as the presence ofsand entering the well and/or wellbore tubular at the depth or depthrange. Thus, the spectral conformance process can be used by itself toidentify the presence of sand ingress in the well.

Before turning to step 622, it can be noted that if either thecomparison in step 614 between the determined spectral centroid and thespectral centroid threshold or the comparison in step 618 between thedetermined spectral spread and the spectral spread threshold results ineither property being below the corresponding threshold, the process mayset an energy value for the sample data set to zero in step 626 beforeallowing the process to proceed to the data integration routine in step628. The spectral conformance checks can occur in any order, and theserial comparisons may allow those sample data sets that fail the firstcomparison of either the spectral centroid or the spectral spread toproceed to the post-processing routine without the need to pass throughthe remaining elements of the spectral conformance process or routine.

Returning to the spectral conformance process or routine 610, the sampledata set can optionally be further processed to allow for thedetermination of a relative amount of sand entering the wellbore at thedepth or depth range represented by the sample data set. In step 622,the sample data set can be filtered to isolate the high frequency,broadband components of the acoustic data. The sample data set can befiltered within a predefined frequency range to produce a second dataset. In an embodiment, the sample data set can be filtered in abandwidth as described herein. For example, the sample data set can befiltered in a frequency bandwidth between about 0.5 kHz to about 10 kHzor between about 2 kHz and about 5 kHz. The frequency filter applied instep 622 may isolate the acoustic signature of the sand ingress whileremoving the lower frequency portions attributable to fluid flow andother potential acoustic sources. The resulting second data set can thenbe processed in step 624 to compute the spectral energy of the seconddata set. In an embodiment, the spectral energy can be calculated as theroot mean square spectral energy of the second data set. The spectralenergy can represent the power or energy of the acoustic signal over thetime period at the depth represented by the second data set. The valueof the determined spectral energy can then be stored in a memory asbeing associated with the depth at the time of collection of theacoustic signal.

In some embodiments, the processing in the spectral conformance processor routine 610 can include determining magnitude and a quality factor ofthe sand ingress peaks in the second data set. The quality factors canthen be used to determine or approximate an amount or rate of sandingress at the location of the peaks. This information can be passed toand stored as part of the event data log.

The resulting determination can then be passed to the data integrationprocessing in step 628. In general, the processing steps determine thepresence of sand ingress at a depth represented by the sample data set.In order to obtain an analysis along the length of the wellbore, theprocessing steps between the data pre-processing steps and the spectralconformance check can be repeated for a plurality of sample data setsrepresenting various depths along the wellbore. As the data is analyzed,the resulting information can pass to the data integration process 628to be integrated into a sand log representing the results along thelength of the wellbore for a given time period. When the data isanalyzed along the length of the wellbore, the process can begin againin order to analyze the data along the length of the wellbore for asubsequent time period. This process can then be repeated as needed totrack the sand ingress in the wellbore over time.

In the data integration process, the data from each analysis can bereceived and used to update an event database in step 630. The data canalso be sent to another database and/or the event database can belocated remotely from the processing location. The data can then befurther analyzed for data integration and visualization in near realtime or at any later time. The data can include the spectral centroid,the spectral spread, the spectral energy (assuming both the spectralcentroid and the spectral spread meet or exceed the correspondingthresholds), or a zero value for the spectral energy when the spectralcentroid, the spectral spread, or both are below the correspondingthreshold, the depth associated with the sample data set, a timeassociated with the acoustic signal acquisition, or any combinationthereof. The data from a plurality of analysis can then be stored in anevent database or log in step 632.

The processing steps in the spectral conformance and storage steps canbe used to reduce the amount of data stored relative to sample data set.In an embodiment, the data stored in the event database in the dataintegration process may have a reduced file size such that a ratio ofthe sample data set file size to the stored data file size can bebetween about 500:1 and about 4,000:1. The overall file size reduction,when taking into account the file reduction in the pre-processing steps605 can result a ratio of the raw acoustic data file size to the datafile size of the data stored in the data integration process of betweenabout 5,000:1 to about 40,000:1 or between about 10,000:1 to about30,000:1. Thus, the process disclosed herein advantageously reduces theamount of raw acoustic data obtained from the wellbore to produce auseful and manageable representation of the sand ingress locations aswell as optionally the relative amount of sand ingress at the sandingress locations.

The data stored in the data integration process can be passed to thedata visualization process 640. In this process, a number of logs can becreated to allow for the visualization and/or representation of the sandingress locations and/or amounts through different times/stages ofproduction. In an embodiment, the data, which can optionally beintegrated in the data integration process 628 but does not have to beintegrated, can be passed to the data visualization process 640. In step642, the spectral energy calculated for a sample data set can beanalyzed to determine if the spectral energy value is greater than zero.In this instance, a zero or null value can be used to indicate that sandingress is not occurring (or at least not occurring at detectablelevels) at the depth. When a zero value is detected, the process canproceed to step 646, where a zero is entered along a well schematic orrepresentation to indicate that sand ingress is not detected at thedepth represented by the sample data set. When the spectral energy valueis not zero, the process can proceed to step 644. In step 644, a visualrepresentation of the spectral energy can be associated with acorresponding depth on a well schematic or representation. The visualrepresentation can be displayed in step 648. From either steps 644 orstep 646, the process can be repeated in step 649 in order to process asubsequent data set or another entry in an integrated log. Once all ofthe data sets and/or entries in the integrated log have been processed,a complete visual representation of sand ingress locations and relativesand ingress rates or amounts along the length of the wellbore can bepresented for a given time. This process can be repeated over aplurality of times to provide and display a real time or near real timerepresentation of sand ingress along the length of the wellbore.

The visualization process 640 can also include the generation anddisplay of a sand ingress log or ‘sand log’. The sand log generallyrepresents the total acoustic power or spectral energy caused by sandingress on one axis and a depth represented by the sample data set onanother axis. This log can be obtained using the integrated log datafrom the data integration process 628 and/or individual data sets can beiteratively analyzed in step 650 to create the integrated sand log. Inthis embodiment, the locations at which no sand ingress is detected canhave a spectral energy set to zero. In step 622, the integrated sand logcan be displayed on a display to provide a representation of thelocations or depths having sand ingress. A plurality of sand logs can becreated for different acoustic data collection times in order to provideand display multiple sand logs in real time or near real time forvarying production settings.

As described above, various actions can be taken based on theidentification of sand ingress locations or locations where sand ingressis not occurring. In some embodiments, the sand ingress identificationmethods can be performed, and no sand ingress locations may be locatedor an amount of sand ingress identified may be below that observed inthe fluid being produced from the wellbore. For example, if sand isidentified within the produced fluid, but no sand ingress locations havebeen identified, it can be determined that the acoustic signal is notdetecting the sand acoustics at a sufficient level to allow for thedetection and location identification. In this instance, the productionrate of the fluid from the wellbore can be temporarily increased. Theresulting data analysis can be performed on the data during theincreased production period while the fluid is being produced. Ingeneral, an increased fluid flow rate into the wellbore may be expectedto increase the acoustic signal intensity at the sand ingress locations.This may allow a signal to noise ratio to be improved in order to moreclearly identify sand ingress at one or more locations by, for example,providing for a greater signal strength to allow the spectralconformance to be determined. The sand energies can also be more clearlycalculated based on the increased signal outputs. Once the zones ofinterest are identified, the production levels can be adjusted based onthe sand ingress locations and amounts.

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor, such as the acquisitiondevice 160 of FIG. 1. FIG. 10 illustrates a computer system 780 suitablefor implementing one or more embodiments disclosed herein such as theacquisition device or any portion thereof. The computer system 780includes a processor 782 (which may be referred to as a centralprocessor unit or CPU) that is in communication with memory devicesincluding secondary storage 784, read only memory (ROM) 786, randomaccess memory (RAM) 788, input/output (I/O) devices 790, and networkconnectivity devices 792. The processor 782 may be implemented as one ormore CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 780, at least one of the CPU 782,the RAM 788, and the ROM 786 are changed, transforming the computersystem 780 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 780 is turned on or booted, the CPU 782may execute a computer program or application. For example, the CPU 782may execute software or firmware stored in the ROM 786 or stored in theRAM 788. In some cases, on boot and/or when the application isinitiated, the CPU 782 may copy the application or portions of theapplication from the secondary storage 784 to the RAM 788 or to memoryspace within the CPU 782 itself, and the CPU 782 may then executeinstructions that the application is comprised of. In some cases, theCPU 782 may copy the application or portions of the application frommemory accessed via the network connectivity devices 792 or via the I/Odevices 790 to the RAM 788 or to memory space within the CPU 782, andthe CPU 782 may then execute instructions that the application iscomprised of. During execution, an application may load instructionsinto the CPU 782, for example load some of the instructions of theapplication into a cache of the CPU 782. In some contexts, anapplication that is executed may be said to configure the CPU 782 to dosomething, e.g., to configure the CPU 782 to perform the function orfunctions promoted by the subject application. When the CPU 782 isconfigured in this way by the application, the CPU 782 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 784 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 788 is not large enough tohold all working data. Secondary storage 784 may be used to storeprograms which are loaded into RAM 788 when such programs are selectedfor execution. The ROM 786 is used to store instructions and perhapsdata which are read during program execution. ROM 786 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 784. The RAM 788 is usedto store volatile data and perhaps to store instructions. Access to bothROM 786 and RAM 788 is typically faster than to secondary storage 784.The secondary storage 784, the RAM 788, and/or the ROM 786 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 790 may include printers, video monitors, liquid crystaldisplays (LCDs), touch screen displays, keyboards, keypads, switches,dials, mice, track balls, voice recognizers, card readers, paper tapereaders, or other well-known input devices.

The network connectivity devices 792 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 792 may enable the processor 782 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 782 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor782, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 782 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several methodswell-known to one skilled in the art. The baseband signal and/or signalembedded in the carrier wave may be referred to in some contexts as atransitory signal.

The processor 782 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 784), flash drive, ROM 786, RAM 788, or the network connectivitydevices 792. While only one processor 782 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 784, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 786, and/or the RAM 788 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 780 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 780 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 780. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 780, atleast portions of the contents of the computer program product to thesecondary storage 784, to the ROM 786, to the RAM 788, and/or to othernon-volatile memory and volatile memory of the computer system 780. Theprocessor 782 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 780. Alternatively, the processor 782may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 792. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 784, to the ROM 786, to the RAM788, and/or to other non-volatile memory and volatile memory of thecomputer system 780.

In some contexts, the secondary storage 784, the ROM 786, and the RAM788 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM788, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 780 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 782 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

Having described various systems and methods herein, specificembodiments can include, but are not limited to:

In a first embodiment, a method of detecting sand inflow into a wellborecomprises: obtaining a sample data set, the sample data set being asample of an acoustic signal originating within a wellbore comprising afluid, detecting a broadband signal within the sample data set, whereinthe broadband signal comprises frequencies greater than about 0.5 kHz,comparing the broadband signal with a signal reference, determining thatthe broadband signal meets or exceeds the signal reference, anddetermining the presence of sand inflow into the wellbore based ondetermining that the broadband signal meets or exceeds the signalreference.

The broadband acoustic signal can include frequencies in the range ofabout 5 Hz to about 10 kHz, frequencies in the range of about 5 Hz toabout 5 kHz or about 50 Hz to about 5 kHz, or frequencies in the rangeof about 500 Hz to about 5 kHz. Any frequency ranges between the lowerfrequencies values (e.g., 5 Hz, 50 Hz, 500 Hz, etc.) and the upperfrequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be used todefine the frequency range for a broadband signal.

A second embodiment can include the method of the first embodiment,wherein detecting a broadband signal comprises: determining a spectralcentroid of the sample data set; determining a spectral spread of thesample data set, wherein the signal reference comprises a spectralcentroid threshold and a spectral spread threshold, and whereindetermining that the broadband signal meets or exceeds the signalreference comprises determining that the spectral centroid is greaterthan a spectral centroid threshold; and determining that the spectralspread is greater than a spectral spread threshold; wherein determiningthe presence of sand inflow into the wellbore based on determining thatthe broadband signal meets or exceeds the signal reference comprisesdetermining the presence of sand inflow into the wellbore based ondetermining that the spectral centroid is greater than a spectralcentroid threshold and determining that the spectral spread is greaterthan a spectral spread threshold. This can be for the case where thesample data set for which the spectral centroid is determined comprisesoptical data indicative of the acoustic signal. Where the sample dataset comprises a derivative of said optical data, it will be understoodthat determining that the broadband signal meets or exceeds the signalreference comprises determining that the spectral centroid is less thana spectral centroid threshold and determining that the spectral spreadis greater than the spectral spread threshold.

In another embodiment, determining that the broadband signal meets orexceeds the signal reference comprises determining a difference betweenthe spectral centroid and the spectral centroid threshold anddetermining that the spectral spread is greater than the spectral spreadthreshold. The sample data set for which the spectral centroid isdetermined can comprise optical data, the intensity of which isproportional to an intensity of the acoustic signal, and determining azero of positive difference between the spectral centroid and thespectral centroid threshold and determining that the spectral spread isgreater than the spectral spread threshold indicates that the signalreference is met or exceeded. Where the sample data set for which thespectral centroid is determined alternatively comprises a derivative ofsaid optical data, determining a zero of negative difference between thespectral centroid and the spectral centroid threshold and determiningthat the spectral spread is greater than the spectral spread thresholdindicates that the signal reference is met or exceeded.

A third embodiment can include the method of the second embodiment,further comprising: producing the fluid from the wellbore at a firstproduction rate; detecting a second acoustic signal within the wellbore;obtaining a second sample data set from the second acoustic signal;determining at least one of a second spectral centroid of the secondsample data set or a second spectral spread of the second sample dataset; determining that the at least one of a second spectral centroid ofthe second sample data set or a second spectral spread of the secondsample data set is less than a corresponding threshold, whereindetermining that the at least one of a second spectral centroid of thesecond sample data set or a second spectral spread of the second sampledata set is less than the corresponding threshold indicates a lack ofsand inflow; and determining that sand is present in the fluid; andincreasing the first production rate of the fluid from the wellbore to asecond production rate, wherein the detecting of the acoustic signalwithin the wellbore occurs at the second production rate.

A fourth embodiment can include the method of the first embodiment,wherein detecting a broadband signal comprises: frequency filtering thesample data set into a plurality of frequency bands, wherein at leastone frequency band of the plurality of frequency bands comprisesfrequencies greater than about 0.5 kHz, wherein detecting the broadbandsignal comprises identifying one or more broadband data sets having anacoustic responses in each of the plurality of frequency bands.

A fifth embodiment can include the method of the fourth embodiment,further comprising: spatially filtering the acoustic signal beforeidentifying the one or more broadband data sets.

A sixth embodiment can include the method of the fourth or fifthembodiment, wherein the signal reference comprises a baseline acousticsignal, and wherein determining that the broadband signal meets orexceeds the signal reference comprises determining that frequencies inthe at least one frequency band comprising frequencies greater thanabout 0.5 kHz have an intensity greater than corresponding frequenciesin the same at least one frequency band of the baseline acoustic signal.

A seventh embodiment can include the method of the sixth embodiment,wherein the baseline acoustic signal is acquired while the wellbore isshut in.

An eighth embodiment can include the method of any of the first toseventh embodiments, wherein obtaining the sample data set comprises:transforming the acoustic signal from a time domain to a frequencydomain to produce the sample data set.

A ninth embodiment can include the method of the fourth embodiment,wherein transforming the acoustic signal comprises: applying a Fouriertransform to at least a portion of the acoustic signal.

A tenth embodiment can include the method of any of the first to ninthembodiments, wherein obtaining the sample data set from the acousticsignal comprises: filtering the acoustic signal using a spatial filterto obtain the sample data set, wherein the sample data set is indicativeof an acoustic sample over a defined depth in the wellbore.

An eleventh embodiment can include the method of any of the first totenth embodiments, wherein the defined depth spatial resolution includesa distance between about 1 meter and about 20 meters.

A twelfth embodiment can include the method of any of the first toeleventh embodiments, further comprising: filtering the sample data setwithin a predefined frequency range to produce a second data set inresponse to determining that the broadband signal meets or exceeds thesignal reference; computing a spectral energy of the second data set;and determining an amount of sand inflow into the wellbore at a defineddepth based on the spectral energy.

A thirteenth embodiment can include the method of any of the first totwelfth embodiments, wherein the predefined frequency range is betweenabout 0.5 kHz and about 5 kHz.

A fourteenth embodiment can include the method of the twelfth orthirteenth embodiment, further comprising: computing a plurality ofspectral energies for a plurality of acoustic data samples along alength of the wellbore, wherein the plurality of spectral energiescomprise the spectral energy of the second data set; and generating asand log comprising the plurality of spectral energies at a plurality ofpoints along the length of the wellbore.

A fifteenth embodiment can include the method of any of the first tofourteenth embodiments, wherein the acoustic signal is detected withinthe wellbore while the fluid is being produced from the wellbore.

In a sixteenth embodiment, a system of detecting sand inflow into awellbore comprises: a processor unit comprising a processor and amemory, wherein the processor unit is adapted for signal communicationwith a receiver, and wherein the memory comprises an analysisapplication, that when executed on the processor, configures theprocessor to: receive, from the receiver, a sample data set, the sampledata set being a sample of an acoustic signal from a wellbore thatcomprises a fluid; detect a broadband signal within the sample data set,wherein the broadband signal comprises frequencies greater than about0.5 kHz, compare the broadband signal with a signal reference; determinethat the broadband signal meets or exceeds the signal reference;determine the presence of sand inflow into the wellbore based ondetermining that the broadband signal meets or exceeds the signalreference; and provide an output indicative of the determination of thepresence of the sand inflow.

A seventeenth embodiment can include the system of the sixteenthembodiment, wherein the analysis application, when executed on theprocessor, further configures the processor to: determine a spectralcentroid of the sample data set; determine a spectral spread of thesample data set; determine that the spectral centroid is greater than aspectral centroid threshold; determine that the spectral spread isgreater than a spectral spread threshold; and determine the inflow ofsand into the wellbore based on determining that the spectral centroidis greater than a spectral centroid threshold and determining that thespectral spread is greater than a spectral spread threshold.

An eighteenth embodiment can include the system of the sixteenthembodiment, when executed on the processor, further configures theprocessor to: frequency filter the sample data set into a plurality offrequency bands, wherein at least one frequency band of the plurality offrequency bands comprises frequencies greater than about 0.5 kHz whereinthe signal reference comprises a baseline acoustic signal; and whereinthe determination that the broadband signal meets or exceeds the signalreference comprises a determination that frequencies in the at least onefrequency band comprising frequencies greater than about 0.5 kHz have anintensity greater than corresponding frequencies in the same at leastone frequency band of the baseline acoustic signal.

A nineteenth embodiment can include the system of the sixteenthembodiment, wherein the analysis application, when executed on theprocessor, configures the processor to: obtain the baseline acousticsample data set while the wellbore is shut in.

A twentieth embodiment can include the system of any of the sixteenth tonineteenth embodiments, wherein the analysis application furtherconfigures the processor to: transform the acoustic signal from a timedomain to a frequency domain to produce the sample data set.

A twenty first embodiment can include the system of any of the sixteenthto twentieth embodiments, wherein the receiver is coupled to adistributed acoustic sensor disposed in the wellbore, wherein thedistributed acoustic sensor system comprises an optical fiber disposedalone at least a portion of a length of the wellbore, and wherein thereceiver is optically coupled to the optical fiber.

A twenty second embodiment can include the system of any of thesixteenth to twenty first embodiments, wherein the analysis applicationfurther configures the processor to: filter the sample data set within apredefined frequency range to produce a second data set in response todetermining that the spectral centroid is greater than a spectralcentroid threshold and in response to determining that the spectralspread is greater than a spectral spread threshold; determine a spectralenergy of the second data set; and determine an amount of sand inflowinto the wellbore at a defined depth based on the spectral energy.

A twenty third embodiment can include the system of any of the sixteenthto twenty second embodiments, further comprising an output device,wherein the analysis application further configures the processor to:generate a log of a plurality of spectral energies at a plurality ofdepths along the wellbore; and display a sand log illustrating theplurality of spectral energies at the plurality of depths.

A twenty fourth embodiment can include the system of any of thesixteenth to twenty third embodiments, further comprising an outputdevice, wherein the analysis application further configures theprocessor to: display time-lapsed sand logs that visualize sanding atdiscrete periods of time.

In a twenty fifth embodiment, a method of detecting sand inflow into awellbore comprises: filtering an acoustic data set using a spatialfilter to obtain a first data sample in the time domain, wherein theacoustic data is obtained from the wellbore, and wherein the first datasample is indicative of an acoustic sample over a defined depth in thewellbore; transforming the first data sample to a frequency domain toproduce a second data sample; determining a spectral centroid of thesecond data sample; determining a spectral spread of the second datasample; determining that the spectral centroid is greater than aspectral centroid threshold; determining that the spectral spread isgreater than a spectral spread threshold; and determining the presenceof sand entering the wellbore at the defined depth based on determiningthat the spectral centroid is greater than a spectral centroid thresholdand determining that the spectral spread is greater than a spectralspread threshold.

A twenty sixth embodiment can include the method of the twenty fifthembodiment, further comprising: filtering the second data sample withina predefined frequency range to produce a third data sample in responseto determining that the spectral centroid is greater than a spectralcentroid threshold and in response to determining that the spectralspread is greater than a spectral spread threshold; computing a spectralenergy of the third data sample.

A twenty seventh embodiment can include the method of the twenty sixthembodiment, further comprising: determining an amount of sand inflowinto the wellbore at the defined depth based on the spectral energy,wherein the spectral energy is indicative of a relative amount of sandentering the wellbore at the defined depth.

A twenty eighth embodiment can include the method of the twelfth ortwenty seventh embodiment, further comprising: changing a productionparameter for the wellbore based on said amount of sand inflow/sandentering the wellbore at the defined depth.

A twenty ninth embodiment can include the method of the twenty eighthembodiment, wherein the production parameter comprises a choke setting.

A thirtieth embodiment can include the method of any of the twenty sixthto twenty ninth embodiments, wherein a ratio of a data size of the thirddata sample to a data size of the acoustic data set is at least 1:1000.

A thirty first embodiment can include the method of any of the twentysixth to thirtieth embodiments, wherein filtering the second data samplecomprises filtering the second data sample in a frequency range ofbetween about 0.5 kHz to about 5 kHz.

A thirty second embodiment can include any of the above mentioned methodembodiments, further comprising: obtaining the acoustic data set oracoustic signal from the wellbore during production of a fluid.

A thirty third embodiment can include the method of any of the twentyfifth to thirty second embodiments, wherein the spatial filter includesa distance between about 1 meter and about 20 meters.

A thirty fourth embodiment can include the method of any of the tenth ortwenty fifth to thirty third embodiments, further comprising: performinga workover at the defined depth based on the determination of thepresence of sand entering the wellbore at the defined depth.

A thirty fifth embodiment can include the method of the thirty fourthembodiment, wherein the workover comprises a consolidation procedure.

A thirty sixth embodiment comprises a method of detecting sand ingresswithin a wellbore, the method comprising:

-   obtaining a sample data set, wherein the sample data set is a sample    of an acoustic signal originating within a wellbore comprising a    fluid, and wherein the sample data set is representative of the    acoustic signal across a frequency spectrum;-   determining a plurality of frequency domain features of the sample    data set;-   determining a presence of sand ingress within the wellbore based on    determining that the plurality of frequency domain features match a    sand ingress signature; and-   estimating a qualitative indication of a concentration of sand at    one or more locations within the wellbore.

A thirty seventh embodiment can include the method of the thirty sixthembodiment wherein determining the presence of sand ingress comprisesdetermining the presence of sand ingress at a plurality of locationswithin the wellbore.

A thirty eighth embodiment can include the method of the thirty sixth orthirty seventh embodiments, wherein estimating the qualitativeindication of the concentration of sand comprises:

determining a peak intensity or power at each location having thepresence of sand ingress for a time period, wherein the qualitativeindication is based on the peak intensity or power at each location.

A thirty ninth embodiment can include the method of the thirty eighthembodiment, wherein estimating the qualitative indication of theconcentration of sand further comprises:

determining an integrated magnitude of each peak; anddetermining a quality factor or width of each peak, wherein thequalitative indication is further based on the integrated magnitude andthe quality factor or width of each peak.

A fortieth embodiment can include the method of the any one of thethirty sixth to thirty ninth embodiments, and can further comprise:

measuring sand produced in a fluid from the well using a surfacemeasurement;proportionally allocating a sand production to each location having thesand ingress based on the measured sand produced in the fluid and therelative qualitative indication of the concentration of sand at eachlocation.

A forty first embodiment can include the method of the any one of thethirty sixth to fortieth embodiments, and can further comprise:

remediating the well at the one or more locations having sand ingress.

A forty second embodiment can include the method of the any one of thethirty sixth to forty first embodiments, and can further comprise;

varying, for example increasing, a production rate from the wellbore;obtaining a second sample data set, wherein the second sample data setis representative of the acoustic signal across the frequency spectrum;determining a second plurality of frequency domain features of thesecond sample data set;re-determining the presence of sand ingress within the wellbore based ondetermining that the second plurality of frequency domain features matchthe sand ingress signature;re-estimating the qualitative indication of a concentration of sand atone or more locations within the wellbore based on the frequency domainfeatures of the sample data set and the frequency domain features of thesecond sample data set.

A forty third embodiment comprises a method of visualizing sand inflowinto a wellbore, the method comprising:

obtaining a sample data set, wherein the sample data set is a sample ofan acoustic signal originating within a wellbore comprising a fluid, andwherein the sample data set is representative of the acoustic signalacross a frequency spectrum,determining a plurality of frequency domain features of the sample dataset;determining a presence of sand ingress at one or more locations withinthe wellbore based on determining that the plurality of frequency domainfeatures match a sand ingress signature;generating a sand log comprising an indication of the sand ingress atthe one or more locations within the wellbore; anddisplaying the sand log.

A forty fourth embodiment can include the method of the forty thirdembodiment, wherein generating the sand log comprises:

calculating an acoustic or spectral energy at each of the one or morelocations for a time period,wherein the sand log comprises a visualization of a depth verses RMSspectral energy graph.

A forty fifth embodiment can include the method of the forty fourthembodiment, wherein generating the sand log comprises:

calculating the acoustic or spectral energy at each of the one or morelocations for a plurality of time periods,wherein displaying the sand log comprises displaying the sand log overthe plurality of time periods.

A forty sixth embodiment can include the method of any one of the fortythird to the forty fifth embodiments, wherein the sand log correlatesone or more production zones in a wellbore with the one or morelocations having the presence of sand ingress.

A forty seventh embodiment can include the method of the forty sixthembodiment and can further comprise:

identifying at least one production zone of the one or more productionzones having the presence of sand ingress using the sand log.

A forty eighth embodiment can include the method of the forty sixth orforty seventh embodiments, and can further comprise:

identifying a relative contribution of sand ingress at each of the oneor more locations using the sand log.

A fiftieth embodiment can include the method of any one of the fortythird to the forty eighth embodiments, wherein each depth location alongthe wellbore not within the one or more locations has an acoustic orspectral energy set to zero within the sand log.

A fifty first embodiment can include a method of remediating a wellbore,the method comprising:

determining a plurality of frequency domain features of a sample dataset, wherein the sample data set is a sample of an acoustic signaloriginating within a wellbore, and wherein the sample data set isrepresentative of the acoustic signal across a frequency spectrum;determining a presence of sand ingress at one or more locations withinthe wellbore based ondetermining that the plurality of frequency domain features match a sandingress signature;performing a remediation procedure at a location of the one or morelocations; andreducing the sand ingress as the location based on performing theremediation procedure.

A fifty second embodiment can include the method of the fifty firstembodiment and can further comprise:

changing a production rate from the wellbore;detecting a change in the a sand ingress rate at the one or morelocations;determining a correlation between the production rate and the sandingress rate at the one or more locations, wherein performing theremediation is based on the correlation.

A fifty third embodiment can include the method of the fifty first orfifty second embodiments, wherein performing the remediation procedurecomprises:

altering an adjustable production sleeve or a choke in a production zonecorresponding to a first location of the one or more locations.

A fifty fourth embodiment can include the method of the fifty first orfifty second embodiments, wherein performing the remediation procedurecomprises:

blocking off an intake sleeve in a production zone corresponding to afirst location of the one or more locations.

A fifty fifth embodiment can include the method of the fifty first orfifty second embodiments, wherein performing the remediation procedurecomprises:

performing a consolidation procedure at a first location of the one ormore locations.

A fifty sixth embodiment can include the method of any one of the fiftyfirst to fifty fifth embodiments and can further comprise:

identifying a first location of the one or more locations,wherein the first location has the highest rate of sand ingress of theone or more locations, andwherein performing the remediation procedure comprises performing theremediation procedure at the first location.

A fifty seventh embodiment comprises a system for detecting sand ingresswithin a wellbore, the system comprising:

a receiver unit comprising a processor and a memory, wherein thereceiver unit is configured to receive a signal from a sensor disposedin a wellbore, wherein a processing application is stored in the memory,and wherein the processing application, when executed on the processor,configures the processor to:receive the signal from the sensor, the signal comprising a sample dataset, which is a sample of an acoustic signal originating within awellbore comprising a fluid, and wherein the sample data set isrepresentative of the acoustic signal across a frequency spectrum;determine a plurality of frequency domain features of the sample dataset;determine a presence of sand ingress within the wellbore based ondetermining that the plurality of frequency domain features match a sandingress signature; andestimate a qualitative indication of a concentration of sand at one ormore locations within the wellbore.

A fifty eighth embodiment comprises a system for visualizing sand inflowinto a wellbore, the system comprising:

a receiver unit comprising a processor and a memory, and a display forvisualising sand inflow into a wellbore, wherein the receiver unit isconfigured to receive a signal from a sensor disposed in the wellbore,wherein a processing application is stored in the memory, and whereinthe processing application, when executed on the processor, configuresthe processor to:receive a sample data set, wherein the sample data set is a sample of anacoustic signal originating within a wellbore comprising a fluid, andwherein the sample data set is representative of the acoustic signalacross a frequency spectrum.determine a plurality of frequency domain features of the sample dataset;determine a presence of sand ingress at one or more locations within thewellbore based on determining that the plurality of frequency domainfeatures match a sand ingress signature; generate a sand log comprisingan indication of the sand ingress at the one or more locations withinthe wellbore; anddisplay the sand log on the display.

A fifty ninth embodiment comprises a system for remediating a wellbore,the system comprising:

a receiver unit comprising a processor and a memory, wherein thereceiver unit is configured to receive a signal from a sensor disposedin the wellbore, wherein a processing application is stored in thememory, and wherein the processing application, when executed on theprocessor, configures the processor to:determine a plurality of frequency domain features of a sample data set,wherein the sample data set is a sample of an acoustic signaloriginating within a wellbore, and wherein the sample data set isrepresentative of the acoustic signal across a frequency spectrum;determine a presence of sand ingress at one or more locations within thewellbore based on determining that the plurality of frequency domainfeatures match a sand ingress signature;determine a remediation procedure to be performed at a location of theone or more locations so as to reduce the sand ingress at the locationbased on performance of the remediation procedure. It will be understoodthat one or more of the method steps disclosed in the above embodimentsmay be performed by the processor of the above embodiments describing asystem.

While various embodiments in accordance with the principles disclosedherein have been shown and described above, modifications thereof may bemade by one skilled in the art without departing from the spirit and theteachings of the disclosure. The embodiments described herein arerepresentative only and are not intended to be limiting. Manyvariations, combinations, and modifications are possible and are withinthe scope of the disclosure. Alternative embodiments that result fromcombining, integrating, and/or omitting features of the embodiment(s)are also within the scope of the disclosure. For example, featuresdescribed as method steps may have corresponding elements in the systemembodiments described above, and vice versa. Accordingly, the scope ofprotection is not limited by the description set out above, but isdefined by the claims which follow, that scope including all equivalentsof the subject matter of the claims. Each and every claim isincorporated as further disclosure into the specification and the claimsare embodiment(s) of the present invention(s). Furthermore, anyadvantages and features described above may relate to specificembodiments, but shall not limit the application of such issued claimsto processes and structures accomplishing any or all of the aboveadvantages or having any or all of the above features.

Additionally, the section headings used herein are provided forconsistency with the suggestions under 37 C.F.R. 1.77 or to otherwiseprovide organizational cues. These headings shall not limit orcharacterize the invention(s) set out in any claims that may issue fromthis disclosure. Specifically and by way of example, although theheadings might refer to a “Field,” the claims should not be limited bythe language chosen under this heading to describe the so-called field.Further, a description of a technology in the “Background” is not to beconstrued as an admission that certain technology is prior art to anyinvention(s) in this disclosure. Neither is the “Summary” to beconsidered as a limiting characterization of the invention(s) set forthin issued claims. Furthermore, any reference in this disclosure to“invention” in the singular should not be used to argue that there isonly a single point of novelty in this disclosure. Multiple inventionsmay be set forth according to the limitations of the multiple claimsissuing from this disclosure, and such claims accordingly define theinvention(s), and their equivalents, that are protected thereby. In allinstances, the scope of the claims shall be considered on their ownmerits in light of this disclosure, but should not be constrained by theheadings set forth herein.

Use of broader terms such as comprises, includes, and having should beunderstood to provide support for narrower terms such as consisting of,consisting essentially of, and comprised substantially of. Use of theterm “optionally,” “may,” “might,” “possibly,” and the like with respectto any element of an embodiment means that the element is not required,or alternatively, the element is required, both alternatives beingwithin the scope of the embodiment(s). Also, references to examples aremerely provided for illustrative purposes, and are not intended to beexclusive.

While preferred embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. For example, the relativedimensions of various parts, the materials from which the various partsare made, and other parameters can be varied. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component, whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

1. A system for processing wellbore data, the system comprising: areceiver unit comprising a processor and a memory, wherein the receiverunit is configured to receive a signal from a sensor disposed in awellbore, wherein a processing application is stored in the memory, andwherein the processing application, when executed on the processor,configures the processor to: receive the signal from the sensor, whereinthe signal comprises an indication of an acoustic signal received at oneor more depths within the wellbore, wherein the signal is indicative ofthe acoustic signal across a frequency spectrum; determine a pluralityof frequency domain features of the signal across the frequencyspectrum; and generate an output comprising the plurality of frequencydomain features.
 2. The system of claim 1, further comprising: thesensor, wherein the sensor comprises a fiber optic cable disposed withinthe wellbore; and an optical generator coupled to the fiber optic cable,wherein the optical generator is configured to generate a light beam andpass the light beam into the fiber optic cable.
 3. The system of claim1, wherein the plurality of frequency domain features of the signalcomprise at least two of: a spectral centroid, a spectral spread, aspectral roll-off, a spectral skewness, an RMS band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, or a spectral autocorrelation function.
 4. The system ofclaim 1, wherein the plurality of frequency domain features of thesignal comprise a spectral centroid, and wherein the spectral centroidis indicative of a center of mass of the frequency spectrum of theacoustic signal.
 5. The system of claim 1, wherein the plurality offrequency domain features of the signal comprise a spectral spread,wherein the spectral spread is indicative of an energy distribution ofthe acoustic signal around a spectral centroid.
 6. The system of claim1, wherein the plurality of frequency domain features of the signalcomprise a spectral roll-off, wherein the spectral roll-off isindicative of a frequency band comprising a predetermined percentage ofa magnitude of signal strengths across the frequency spectrum.
 7. Thesystem of claim 1, wherein the plurality of frequency domain features ofthe signal comprise a spectral skewness, wherein the spectral skewnessis indicative of a symmetry of a distribution of spectral magnitudevalues around an arithmetic mean of the spectral magnitude values. 8.The system of claim 1, wherein the plurality of frequency domainfeatures of the signal comprise an RMS band energy, wherein the RMS bandenergy is a measure of signal energy of the signal in predeterminedfrequency bands across the frequency spectrum.
 9. The system of claim 8,wherein the signal energy in each frequency band of the predeterminedfrequency bands is a normalized energy based on a total RMS energyacross the frequency spectrum.
 10. The system of claim 1, wherein theplurality of frequency domain features of the signal comprise a totalRMS energy, wherein the total RMS energy comprises a root mean square ofa waveform of the signal calculated in the time domain.
 11. The systemof claim 1, wherein the plurality of frequency domain features of thesignal comprise a spectral flatness, wherein the spectral flatness is aratio of a geometric mean to an arithmetic mean of an energy spectrumvalue of the signal.
 12. The system of claim 1, wherein the plurality offrequency domain features of the signal comprise a spectral slope,wherein the spectral slope comprises a linear approximation of a shapeof the spectrum of the signal.
 13. The system of claim 1, wherein theplurality of frequency domain features of the signal comprise a spectralkurtosis, wherein the spectral kurtosis comprises an indication of aflatness of the spectrum around a mean of the spectrum in the signal.14. The system of claim 1, wherein the plurality of frequency domainfeatures of the signal comprise a spectral flux, wherein the spectralflux is a measure of a change in spectral magnitude summed across atleast a portion of frequencies present in the signal between successivedeterminations of the frequency domain features.
 15. The system of claim1, wherein the plurality of frequency domain features of the signalcomprise a spectral autocorrelation function, wherein the spectralautocorrelation function is indicative of a lag of the signal thatmaximizes the correlation between the signal and a shifted signal. 16.The system of claim 1, wherein the signal comprises a first data size,wherein the output comprises a second data size, and wherein the firstdata size is greater than the second data size.
 17. A system fordetecting an event within a wellbore, the system comprising: a processorunit comprising a processor and a memory, wherein the processor unit isadapted for signal communication with a receiver, and wherein the memorycomprises an analysis application, that when executed on the processor,configures the processor to: receive, from the receiver, a signalcomprising a plurality of frequency domain features, wherein thefrequency domain features are indicative of an acoustic signal within awellbore, and wherein the frequency domain features are indicative ofthe acoustic signal across a frequency spectrum; compare the pluralityof frequency domain features with one or more event signatures, whereinthe one or more event signatures comprise thresholds or ranges for eachof the plurality of frequency domain features; determine that theplurality of frequency domain features match at least one eventsignature of the one or more event signatures; determine the occurrenceof at least one event based on the determination that the plurality offrequency domain features match the at least one event signature; andgenerate an output of the occurrence of the at least one event based onthe determination.
 18. The system of claim 17, wherein the plurality offrequency domain features comprise a spectral centroid and a spectralspread, wherein the one or more event signatures comprise a sand ingresssignature that comprises a spectral centroid threshold and a spectralspread threshold, and wherein the processor is configured to: determinea difference between the spectral centroid and the spectral centroidthreshold; determine that the spectral spread is greater than thespectral spread threshold; and wherein the determination of theoccurrence of the at least one event comprises a determination of theinflow of sand into the wellbore based whether the difference betweenthe spectral centroid and the spectral centroid threshold is positive ornegative and the determination that the spectral spread is greater thanthe spectral spread threshold.
 19. The system of claim 18, wherein thespectral centroid threshold of the sand ingress signature is in a rangebetween about 0.5 kHz to about 5 kHz.
 20. The system of claim 17,wherein the one or more event signatures comprise a gas leak signature,wherein the gas leak signature is indicative of a gas leak from aformation in the wellbore through a leak path, and wherein thedetermination of the occurrence of the at least one event comprises adetermination of the gas leak from the formation to the annulus in thewellbore.
 21. The system of claim 17, wherein the one or more eventsignatures comprise a gas influx signature, wherein the gas influxsignature is indicative of a gas inflow from a formation into thewellbore, and wherein the determination of the occurrence of the atleast one event comprises a determination of the gas influx from theformation to the wellbore.
 22. The system of claim 17, wherein the oneor more event signatures comprise a liquid inflow signature, wherein theliquid inflow signature is indicative of a liquid inflow from aformation into the wellbore, and wherein the determination of theoccurrence of the at least one event comprises a determination of theliquid inflow from the formation to the wellbore.
 23. The system ofclaim 17, wherein the one or more event signatures comprise a sandtransport signature, wherein the sand transport signature is indicativeof sand flowing within a carrier fluid within the wellbore, and whereinthe determination of the occurrence of the at least one event comprisesa determination of the sand transport within the wellbore.
 24. Thesystem of claim 17, wherein the one or more event signatures comprise asand restriction signature, wherein the sand restriction signature isindicative of a liquid flow past a sand restriction in a tubular withinthe wellbore, and wherein the determination of the occurrence of the atleast one event comprises a determination of the sand restriction in thetubular.
 25. The system of claim 17, wherein the one or more eventsignatures comprise a casing fluid flow signature, wherein the casingfluid flow signature is indicative of a fluid flow between a casing anda formation, and wherein the determination of the occurrence of the atleast one event comprises a determination of the fluid flow between thecasing and the formation.
 26. The system of claim 17, wherein the one ormore event signatures comprise a self-induced hydraulic fracturing,wherein the self-induced hydraulic fracturing signature is indicative ofa formation of a self-induced fracture within a formation, and whereinthe determination of the occurrence of the at least one event comprisesa determination of the formation of the self-induced hydraulic fracture.27. The system of claim 17, wherein the one or more event signaturescomprise a fluid leak signature, wherein the fluid leak signature isindicative of a liquid flow past a downhole plug within the wellbore,and wherein the determination of the occurrence of the at least oneevent comprises a determination of the liquid flow past the downholeplug within the wellbore.
 28. The system of claim 17, wherein the one ormore event signatures comprise a fracturing signature, wherein thefracturing signature is indicative of a formation of a fracturing withina formation, and wherein the determination of the occurrence of the atleast one event comprises a determination of the formation of thefracture.
 29. A method of detecting an event within a wellbore, themethod comprising: obtaining a sample data set, wherein the sample dataset is a sample of an acoustic signal originating within a wellborecomprising a fluid, and wherein the sample data set is representative ofthe acoustic signal across a frequency spectrum. determining a pluralityof frequency domain features of the sample data set; comparing theplurality of frequency domain features with an event signature, whereinthe event signature comprises a plurality of thresholds, ranges, or bothcorresponding to the plurality of frequency domain features; determiningthat the plurality of frequency domain features matches the thresholds,ranges, or both of the event signature; and determining the presence ofthe event within the wellbore based on determining that the plurality offrequency domain features match the thresholds, ranges, or both of theevent signature.
 30. The method of claim 29, wherein the at least onespectral characteristic is determined for the acoustic signal across thefrequency spectrum.
 31. The method of claim 29, wherein the eventcomprises at least one of: a gas leak from a subterranean formation intoan annulus in the wellbore, a gas inflow from the subterranean formationinto the wellbore, sand ingress into the wellbore, a liquid inflow intothe wellbore, sand transport within a tubular in the wellbore, fluidflow past a sand plug in a tubular in the wellbore, fluid flow behind acasing, a self-induced hydraulic fracture within the subterraneanformation, a fluid leak past a downhole seal, or a rock fracturepropagation event.
 32. The method of claim 29, wherein the one or morefrequency domain features comprise a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, or a spectral autocorrelation function. 33.The method of claim 29, further comprising: reducing a size of thesample data set by at least 1000 times before comparing the at least onespectral characteristic of the one or more frequency domain featureswith the event signature.
 34. The method of claim 29, wherein obtainingthe sample data set comprises: passing a light pulse into a fiber opticcable, wherein the fiber optic cable is disposed in the wellbore;receiving a reflection from the light pulse; and processing thereflection to generate the sample data set.
 35. The method of claim 29,wherein the sample data set comprises acoustic frequencies greater thanabout 0.5 kHz, and wherein the at least one spectral characteristiccomprises a spectral centroid of the sample data set and a spectralspread of the sample data set, wherein the event signature comprises aspectral centroid threshold and a spectral spread threshold, and whereindetermining that the at least one spectral characteristic matches theevent signature comprises: determining a difference between the spectralcentroid and a spectral centroid threshold; determining that thespectral spread is greater than a spectral spread threshold; anddetermining a presence of sand inflow into the wellbore based ondetermining that the at least one spectral characteristic matches theevent signature.
 36. The method of claim 35, further comprising:producing the fluid from the wellbore at a first production rate;detecting a second acoustic signal within the wellbore; obtaining asecond sample data set from the second acoustic signal; determining atleast one of a second spectral centroid of the second sample data set ora second spectral spread of the second sample data set; determining thatthe at least one of a second spectral centroid of the second sample dataset or a second spectral spread of the second sample data set does notmeet or exceed a corresponding threshold of the event signature, whereindetermining that the at least one of a second spectral centroid of thesecond sample data set or a second spectral spread of the second sampledata set does not meet or exceed the corresponding threshold indicates alack of sand inflow; determining that sand is present in the fluid; andincreasing the first production rate of the fluid from the wellbore to asecond production rate, wherein the detecting of the acoustic signalwithin the wellbore occurs at the second production rate.
 37. The methodof claim 29, wherein the event signature comprises a gas leak signaturewherein the gas leak signature is indicative of a gas leak from aformation through a leak path in the wellbore, and determining thepresence of the event comprises determining the presence of the gas leakthrough a leak path in the wellbore.
 38. The method of claim 29, whereinthe event signature comprises a gas influx signature, wherein the gasinflux signature is indicative of a gas inflow from a formation into thewellbore, and wherein determining the presence of the event comprisesdetermining the presence of the gas influx from the formation to thewellbore.
 39. The method of claim 29, wherein the event signaturecomprises a liquid inflow signature, wherein the liquid inflow signatureis indicative of a liquid inflow from a formation into the wellbore, andwherein determining the presence of the event comprises determining thepresence of the liquid inflow from the formation to the wellbore. 40.The method of claim 29, wherein the event signature comprises a sandtransport signature, wherein the sand transport signature is indicativeof sand flowing within a carrier fluid within the wellbore, and whereindetermining the presence of the event comprises determining the presenceof sand transport within the wellbore.
 41. The method of claim 29,wherein the event signature comprises a sand restriction signature,wherein the sand restriction signature is indicative of a liquid flowpast a sand restriction in a tubular within the wellbore, and whereindetermining the presence of the event comprises determining the presenceof the sand restriction in the tubular.
 42. The method of claim 29,wherein the event signature comprises a casing fluid flow signature,wherein the casing fluid flow signature is indicative of a fluid flowbetween a casing and a formation, and wherein determining the presenceof the event comprises determining the presence of the fluid flowbetween the casing and the formation.
 43. The method of claim 29,wherein the event signature comprises a self-induced hydraulicfracturing signature, wherein the self-induced hydraulic fracturingsignature is indicative of a formation of a self-induced fracture withina formation, and wherein determining the presence of the event comprisesdetermining the presence of the formation of the self-induced hydraulicfracture.
 44. The method of claim 29, wherein the event signaturecomprises a fluid leak signature, wherein the fluid leak signature isindicative of a liquid flow past a downhole plug within the wellbore,and wherein determining the presence of the event comprises determiningthe presence of the liquid flow past the downhole plug within thewellbore.
 45. The method of claim 29, wherein the event signaturecomprises a fracturing signature, wherein the fracturing signature isindicative of a formation of a fracturing within a formation, andwherein determining the presence of the event comprises determining thepresence of the formation of the fracture.